Helping authors produce FAIR taxonomic data: evaluation of an author-driven phenotype data production prototype
Limin Zhang, Julian Starr, Bruce Ford, Anton Reznicek, Yuxuan Zhou, Étienne Léveillé-Bourret, Étienne Lacroix-Carignan, Jacques Cayouette, Tyler W Smith, Donald Sutherland, Paul Catling, Jeffery M Saarela, Hong Cui, James Macklin
TL;DR
This paper introduces a prototype system to help biologists create standardized, FAIR-compliant phenotype data using ontologies and evaluates its effectiveness with students and experts.
Contribution
A prototype system with ontology-enhanced tools for FAIR phenotype data production and its evaluation with users.
Findings
Character Recorder was found to be quickly learnable and comparable to Excel in cognitive demand.
Users produced higher-quality data with Character Recorder compared to Excel.
Experts recommended and supported the tool's development into a comprehensive system.
Abstract
It is well-known that the use of vocabulary in phenotype treatments is often inconsistent. An earlier survey of biologists who create or use phenotypic characters revealed that this lack of standardization leads to ambiguities, frustrating both the consumers and producers of phenotypic data. Such ambiguities are challenging for biologists, and more so for Artificial Intelligence, to resolve. That survey also indicated a strong interest in a new authoring workflow supported by ontologies to ensure published phenotype data are FAIR (Findable, Accessible, Interoperable, and Reusable) and suitable for large-scale computational analyses. In this article, we introduce a prototype software system designed for authors to produce computational phenotype data. This platform includes a web-based, ontology-enhanced editor for taxonomic characters (Character Recorder), an Ontology Backend holding…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Species Distribution and Climate Change · Research Data Management Practices