ArticleDatabase : the journal of biological databases and curation2025
Helping authors produce FAIR taxonomic data: evaluation of an author-driven phenotype data production prototype.
Article in Database : the journal of biological databases and curation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- A scalable exemplar-based method for aligning biological taxonomies.Biodiversity data journal · 2026Article
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Authors and funding
14 authors.
Funding
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 standardized vocabulary (the Cared Ontology), and a mobile application for resolving ontological conflicts (Conflict Resolver). We present two formal user evaluations of Character Recorder, the main interface authors would interact with to produce FAIR data. The evaluations were conducted with undergraduate biology students and Carex experts. We evaluated Character Recorder against Microsoft Excel on their effectiveness, efficiency, and the cognitive demands of the users in producing computable taxon-by-character matrices. The evaluations showed that Character Recorder is quickly learnable for both student and professional participants, with its cognitive demand comparable to Excel's. Participants agreed that the quality of the data Character Recorder yielded was superior. Students praised Character Recorder's educational value, while Carex experts were keen to recommend it and help evolve it from a prototype into a comprehensive tool. Feature improvements recommended by expert participants have been implemented after the evaluation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.