Search for: All records

Abstract Critical to answering large-scale questions in biology is the integration of knowledge from different disciplines into a coherent, computable whole. Controlled vocabularies such as ontologies represent a clear path toward this goal. Using survey questionnaires, we examined the attitudes of biologists toward adopting controlled vocabularies in phenotype publications. Our questions cover current experience and overall attitude with controlled vocabularies, the awareness of the issues around ambiguity and inconsistency in phenotype descriptions and post-publication professional data curation, the preferred solutions and the effort and desired rewards for adopting a new authoring workflow. Results suggest that although the existence of controlled vocabularies is widespread, their use is not common. A majority of respondents (74%) are frustrated with ambiguity in phenotypic descriptions, and there is a strong agreement (mean agreement score 4.21 out of 5) that author curation would better reflect the original meaning of phenotype data. Moreover, the vast majority (85%) of researchers would try a new authoring workflow if resultant data were more consistent and less ambiguous. Even more respondents (93%) suggested that they would try and possibly adopt a new authoring workflow if it required 5% additional effort as compared to normal, but higher rates resulted in a steep decline in likely adoption rates. Among the four different types of rewards, two types of citations were the most desired incentives for authors to produce computable data. Overall, our results suggest the adoption of a new authoring workflow would be accelerated by a user-friendly and efficient software-authoring tool, an increased awareness of the challenges text ambiguity creates for external curators and an elevated appreciation of the benefits of controlled vocabularies. 

It takes great effort to manually or semi-automatically convert free-text phenotype narratives (e.g., morphological descriptions in taxonomic works) to a computable format before they can be used in large-scale analyses. We argue that neither a manual curation approach nor an information extraction approach based on machine learning is a sustainable solution to produce computable phenotypic data that are FAIR (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al. 2016). This is because these approaches do not scale to all biodiversity, and they do not stop the publication of free-text phenotypes that would need post-publication curation. In addition, both manual and machine learning approaches face great challenges: the problem of inter-curator variation (curators interpret/convert a phenotype differently from each other) in manual curation, and keywords to ontology concept translation in automated information extraction, make it difficult for either approach to produce data that are truly FAIR. Our empirical studies show that inter-curator variation in translating phenotype characters to Entity-Quality statements (Mabee et al. 2007) is as high as 40% even within a single project. With this level of variation, curated data integrated from multiple curation projects may still not be FAIR. The key causes of this variation have been identified as semantic vagueness in original phenotype descriptions and difficulties in using standardized vocabularies (ontologies). We argue that the authors describing characters are the key to the solution. Given the right tools and appropriate attribution, the authors should be in charge of developing a project's semantics and ontology. This will speed up ontology development and improve the semantic clarity of the descriptions from the moment of publication. In this presentation, we will introduce the Platform for Author-Driven Computable Data and Ontology Production for Taxonomists, which consists of three components: a web-based, ontology-aware software application called 'Character Recorder,' which features a spreadsheet as the data entry platform and provides authors with the flexibility of using their preferred terminology in recording characters for a set of specimens (this application also facilitates semantic clarity and consistency across species descriptions); a set of services that produce RDF graph data, collects terms added by authors, detects potential conflicts between terms, dispatches conflicts to the third component and updates the ontology with resolutions; and an Android mobile application, 'Conflict Resolver,' which displays ontological conflicts and accepts solutions proposed by multiple experts. a web-based, ontology-aware software application called 'Character Recorder,' which features a spreadsheet as the data entry platform and provides authors with the flexibility of using their preferred terminology in recording characters for a set of specimens (this application also facilitates semantic clarity and consistency across species descriptions); a set of services that produce RDF graph data, collects terms added by authors, detects potential conflicts between terms, dispatches conflicts to the third component and updates the ontology with resolutions; and an Android mobile application, 'Conflict Resolver,' which displays ontological conflicts and accepts solutions proposed by multiple experts. Fig. 1 shows the system diagram of the platform. The presentation will consist of: a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). The key difference of this system from other ontology-aware systems is that authors can directly add needed terms to the ontology as they wish and can update their data according to ontology updates. a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). The key difference of this system from other ontology-aware systems is that authors can directly add needed terms to the ontology as they wish and can update their data according to ontology updates. The software modules currently incorporated in Character Recorder and Conflict Resolver have undergone formal usability studies. We are actively recruiting Carex experts to participate in a 3-day usability study of the entire system of the Platform for Author-Driven Computable Data and Ontology Production for Taxonomists. Participants will use the platform to record 100 characters about one Carex species. In addition to usability data, we will collect the terms that participants submit to the underlying ontology and the data related to conflict resolution. Such data allow us to examine the types and the quantities of logical conflicts that may result from the terms added by the users and to use Discrete Event Simulation models to understand if and how term additions and conflict resolutions converge. We look forward to a discussion on how the tools (Character Recorder is online at http://shark.sbs.arizona.edu/chrecorder/public) described in our presentation can contribute to producing and publishing FAIR data in taxonomic studies. 

Abstract Producing findable, accessible, interoperable and reusable (FAIR) data cannot be accomplished solely by data curators in all disciplines. In biology, we have shown that phenotypic data curation is not only costly, but it is burdened with inter-curator variation. We intend to propose a software platform that would enable all data producers, including authors of scientific publications, to produce ontologized data at the time of publication. Working toward this goal, we need to identify ontology construction methods that are preferred by end users. Here, we employ two usability studies to evaluate effectiveness, efficiency and user satisfaction with a set of four methods that allow an end user to add terms and their relations to an ontology. Thirty-three participants took part in a controlled experiment where they evaluated the four methods (Quick Form, Wizard, WebProtégé and Wikidata) after watching demonstration videos and completing a hands-on task. Another think-aloud study was conducted with three professional botanists. The efficiency effectiveness and user confidence in the methods are clearly revealed through statistical and content analyses of participants’ comments. Quick Form, Wizard and WebProtégé offer distinct strengths that would benefit our author-driven FAIR data generation system. Features preferred by the participants will guide the design of future iterations.