title: Moving the mountain: analysis of the effort required to transform comparative anatomy into computable anatomy.
abstract: The diverse phenotypes of living organisms have been described for centuries, and though they may be digitized, they are not readily available in a computable form. Using over 100 morphological studies, the Phenoscape project has demonstrated that by annotating characters with community ontology terms, links between novel species anatomy and the genes that may underlie them can be made. But given the enormity of the legacy literature, how can this largely unexploited wealth of descriptive data be rendered amenable to large-scale computation? To identify the bottlenecks, we quantified the time involved in the major aspects of phenotype curation as we annotated characters from the vertebrate phylogenetic systematics literature. This involves attaching fully computable logical expressions consisting of ontology terms to the descriptions in character-by-taxon matrices. The workflow consists of: (i) data preparation, (ii) phenotype annotation, (iii) ontology development and (iv) curation team discussions and software development feedback. Our results showed that the completion of this work required two person-years by a team of two post-docs, a lead data curator, and students. Manual data preparation required close to 13% of the effort. This part in particular could be reduced substantially with better community data practices, such as depositing fully populated matrices in public repositories. Phenotype annotation required ∼40% of the effort. We are working to make this more efficient with Natural Language Processing tools. Ontology development (40%), however, remains a highly manual task requiring domain (anatomical) expertise and use of specialized software. The large overhead required for data preparation and ontology development contributed to a low annotation rate of approximately two characters per hour, compared with 14 characters per hour when activity was restricted to character annotation. Unlocking the potential of the vast stores of morphological descriptions requires better tools for efficiently processing natural language, and better community practices towards a born-digital morphology. Database URL: http://kb.phenoscape.org
authors.affiliation: Department of Biology, University of South Dakota, Vermillion, SD, USA, Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA and National Evolutionary Synthesis Center, Durham, NC, USA Wasila.Dahdul@usd.edu.
authors.forename: Wasila
authors.initials: W
authors.lastname: Dahdul
authors.affiliation: Department of Biology, University of South Dakota, Vermillion, SD, USA, Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA and National Evolutionary Synthesis Center, Durham, NC, USA.
authors.forename: T Alexander
authors.initials: TA
authors.lastname: Dececchi
authors.affiliation: Department of Biology, University of South Dakota, Vermillion, SD, USA, Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA and National Evolutionary Synthesis Center, Durham, NC, USA.
authors.forename: Nizar
authors.initials: N
authors.lastname: Ibrahim
authors.affiliation: Department of Biology, University of South Dakota, Vermillion, SD, USA, Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA and National Evolutionary Synthesis Center, Durham, NC, USA.
authors.forename: Hilmar
authors.initials: H
authors.lastname: Lapp
authors.affiliation: Department of Biology, University of South Dakota, Vermillion, SD, USA, Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA and National Evolutionary Synthesis Center, Durham, NC, USA.
authors.forename: Paula
authors.initials: P
authors.lastname: Mabee
mesh_terms: D000717:Anatomy, Comparative; D000818:Animals; D064229:Biological Ontologies; D066289:Data Curation / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D009323:Natural Language Processing