Leonelli, Sabina (2022) Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science. Philosophy, theory and practice in biology, 14.
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Abstract
This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants: particularly the biological, cultural, scientific and semantic diversity affecting the identification and description of plant traits, the methods used to generate and process data, and the goals and skills of those with relevant expertise—including farmers and breeders. Through a study of the Crop Ontology (which explicitly recognizes and negotiates diversity) and its application to cassava breeding, I argue for a process-sensitive approach to the naming of plant traits that focuses on documenting environmental processes instead of biological products. I claim that this approach can foster reliable linkage and robust re-use of plant data, while at the same time facilitating dialogue between data scientists, plant researchers, breeders, and other relevant experts in ways that crucially inform agricultural interventions. I conclude that the study of data semantics and related descriptors constitutes a productive and underexplored way to think about the epistemic import of naming traits within plant science. The effort to articulate semantic differences among plant varieties and methods of data processing can generate newly inclusive ways to develop and communicate biological knowledge. In turn, such practices have the potential to defy existing understandings of systematisation and hierarchies of expertise in biology, thus bolstering the extent to which plant science can support biodiversity and sustainable agriculture.
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| Item Type: | Published Article or Volume | ||||||
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| Keywords: | data science, phenomics, diversity, computational ontologies, databases, plant traits, classification, taxonomy, precision agriculture | ||||||
| Subjects: | General Issues > Data Specific Sciences > Biology General Issues > Evidence General Issues > Theory/Observation |
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| Depositing User: | Sabina Leonelli | ||||||
| Date Deposited: | 09 Feb 2023 18:48 | ||||||
| Last Modified: | 09 Feb 2023 18:48 | ||||||
| Item ID: | 21742 | ||||||
| Journal or Publication Title: | Philosophy, theory and practice in biology | ||||||
| DOI or Unique Handle: | https://doi.org/10.3998/ptpbio.3364 | ||||||
| Subjects: | General Issues > Data Specific Sciences > Biology General Issues > Evidence General Issues > Theory/Observation |
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| Date: | 2022 | ||||||
| Volume: | 14 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/21742 |
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