Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science
dc.contributor.author | Leonelli, S | |
dc.date.accessioned | 2022-09-01T09:49:31Z | |
dc.date.issued | 2022-10-21 | |
dc.date.updated | 2022-08-25T12:05:10Z | |
dc.description.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. | en_GB |
dc.description.sponsorship | European Research Council (ERC) | en_GB |
dc.description.sponsorship | Australian Research Council (ARC) | en_GB |
dc.description.sponsorship | Alan Turing Institute | en_GB |
dc.description.sponsorship | Wissenschaftskolleg zu Berlin | en_GB |
dc.identifier.citation | Vol. 14, article 16 | en_GB |
dc.identifier.doi | 10.3998/ptpbio.3364 | |
dc.identifier.grantnumber | 335925 | en_GB |
dc.identifier.grantnumber | 101001145 | en_GB |
dc.identifier.grantnumber | DP160102989 | en_GB |
dc.identifier.grantnumber | EP/N510129/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/130633 | |
dc.identifier | ORCID: 0000-0002-7815-6609 (Leonelli, Sabina) | |
dc.language.iso | en | en_GB |
dc.publisher | Michigan Publishing | en_GB |
dc.rights | © 2022 Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits anyone to download, copy, distribute, display, or adapt the text without asking for permission, provided that the creator(s) are given full credit. | |
dc.subject | data science | en_GB |
dc.subject | henomics | en_GB |
dc.subject | diversity | en_GB |
dc.subject | computational ontologies | en_GB |
dc.subject | databases | en_GB |
dc.subject | plant traits | en_GB |
dc.subject | classification | en_GB |
dc.subject | taxonomy | en_GB |
dc.subject | precision agriculture | en_GB |
dc.title | Process-Sensitive Naming: Trait Descriptors and the Shifting Semantics of Plant (Data) Science | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-09-01T09:49:31Z | |
dc.description | This is the final version. Available on open access from Michigan Publishing via the DOI in this record | en_GB |
dc.identifier.eissn | 2475-3025 | |
dc.identifier.journal | Philosophy, Theory and Practice in Biology | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-08-08 | |
dcterms.dateSubmitted | 2022-01-25 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-08-08 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-08-25T12:05:13Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-11-23T15:07:10Z | |
refterms.panel | C | en_GB |
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This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0
International license, which permits anyone to download, copy, distribute, display, or adapt the text
without asking for permission, provided that the creator(s) are given full credit.