More Like this

1. Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium

   https://doi.org/10.1093/database/baad076  

   Clarke, Jennifer L ; Cooper, Laurel D ; Poelchau, Monica F ; Berardini, Tanya Z ; Elser, Justin ; Farmer, Andrew D ; Ficklin, Stephen ; Kumari, Sunita ; Laporte, Marie-Angélique ; Nelson, Rex T ; et al ( January 2023 , Database)

   Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as ‘databases’ throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means.

   Database URL https://www.agbiodata.org/databases

    

   more » « less
2. Author-Driven Computable Data and Ontology Production for Taxonomists

   https://doi.org/10.3897/biss.5.75741  

   Cui, Hong ; Ford, Bruce ; Starr, Julian ; Macklin, James ; Reznicek, Anton ; Giebink, Noah ; Longert, Dylan ; Léveillé-Bourret, Étienne ; Zhang, Limin ( September 2021 , Biodiversity Information Science and Standards)

   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. 

   more » « less
3. Incentivising use of structured language in biological descriptions: Author-driven phenotype data and ontology production

   https://doi.org/10.3897/BDJ.6.e29616  

   Cui, Hong ; Macklin, James ; Sachs, Joel ; Reznicek, Anton ; Starr, Julian ; Ford, Bruce ; Penev, Lyubomir ; Chen, Hsin-Liang ( November 2018 , Biodiversity Data Journal)

   Phenotypes are used for a multitude of purposes such as defining species, reconstructing phylogenies, diagnosing diseases or improving crop and animal productivity, but most of this phenotypic data is published in free-text narratives that are not computable. This means that the complex relationship between the genome, the environment and phenotypes is largely inaccessible to analysis and important questions related to the evolution of organisms, their diseases or their response to climate change cannot be fully addressed. It takes great effort to manually convert free-text narratives to a computable format before they can be used in large-scale analyses. We argue that this manual curation approach is not a sustainable solution to produce computable phenotypic data for three reasons: 1) it does not scale to all of biodiversity; 2) it does not stop the publication of free-text phenotypes that will continue to need manual curation in the future and, most importantly, 3) It does not solve the problem of inter-curator variation (curators interpret/convert a phenotype differently from each other). Our empirical studies have shown that inter-curator variation is as high as 40% even within a single project. With this level of variation, it is difficult to imagine that data integrated from multiple curation projects can be of high quality. The key causes of this variation have been identified as semantic vagueness in original phenotype descriptions and difficulties in using standardised vocabularies (ontologies). We argue that the authors describing phenotypes 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 phenotype descriptions from the moment of publication. A proof of concept project on this idea was funded by NSF ABI in July 2017. We seek readers input or critique of the proposed approaches to help achieve community-based computable phenotype data production in the near future. Results from this project will be accessible through https://biosemantics.github.io/author-driven-production. 

   more » « less
4. Formalizing Invertebrate Morphological Data: A Descriptive Model for Cuticle-Based Skeleto-Muscular Systems, an Ontology for Insect Anatomy, and their Potential Applications in Biodiversity Research and Informatics

   https://doi.org/10.1093/sysbio/syad025  

   Girón, Jennifer C ; Tarasov, Sergei ; González Montaña, Luis Antonio ; Matentzoglu, Nicolas ; Smith, Aaron D ; Koch, Markus ; Boudinot, Brendon E ; Bouchard, Patrice ; Burks, Roger ; Vogt, Lars ; et al ( April 2023 , Systematic Biology)

   Abstract The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multi-species anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to (1) use controlled vocabularies and create semi-automated computer-parsable insect morphological descriptions; (2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and (3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies. 

   more » « less
5. Indexing Biotic Interactions in GBIF data

   https://doi.org/10.3897/biss.6.93565  

   Salim, José Augusto ; Seltmann, Katja ; Poelen, Jorrit ; Saraiva, Antonio ( August 2022 , Biodiversity Information Science and Standards)

   The Global Biodiversity Information Facility (GBIF 2022a) has indexed more than 2 billion occurrence records from 70,147 datasets. These datasets often include "hidden" biotic interaction data because biodiversity communities use the Darwin Core standard (DwC, Wieczorek et al. 2012) in different ways to document biotic interactions. In this study, we extracted biotic interactions from GBIF data using an approach similar to that employed in the Global Biotic Interactions (GloBI; Poelen et al. 2014) and summarized the results. Here we aim to present an estimation of the interaction data available in GBIF, showing that biotic interaction claims can be automatically found and extracted from GBIF. Our results suggest that much can be gained by an increased focus on development of tools that help to index and curate biotic interaction data in existing datasets. Combined with data standardization and best practices for sharing biotic interactions, such as the initiative on plant-pollinators interaction (Salim 2022), this approach can rapidly contribute to and meet open data principles (Wilkinson 2016). We used Preston (Elliott et al. 2020), open-source software that versions biodiversity datasets, to copy all GBIF-indexed datasets. The biodiversity data graph version (Poelen 2020) of the GBIF-indexed datasets used during this study contains 58,504 datasets in Darwin Core Archive (DwC-A) format, totaling 574,715,196 records. After retrieval and verification, the datasets were processed using Elton. Elton extracts biotic interaction data and supports 20+ existing file formats, including various types of data elements in DwC records. Elton also helps align interaction claims (e.g., host of, parasite of, associated with) to the Relations Ontology (RO, Mungall 2022), making it easier to discover datasets across a heterogeneous collection of datasets. Using specific mapping between interaction claims found in the DwC records to the terms in RO*1, Elton found 30,167,984 potential records (with non-empty values for the scanned DwC terms) and 15,248,478 records with recognized interaction types. Taxonomic name validation was performed using Nomer, which maps input names to names found in a variety of taxonomic catalogs. We only considered an interaction record valid where the interaction type could be mapped to a term in RO and where Nomer found a valid name for source and target taxa. Based on the workflow described in Fig. 1, we found 7,947,822 interaction records (52% of the potential interactions). Most of them were generic interactions ( interacts_ with , 87.5%), but the remaining 12.5% (993,477 records) included host-parasite and plant-animal interactions. The majority of the interactions records found involved plants (78%), animals (14%) and fungi (6%). In conclusion, there are many biotic interactions embedded in existing datasets registered in large biodiversity data indexers and aggregators like iDigBio, GBIF, and BioCASE. We exposed these biotic interaction claims using the combined functionality of biodiversity data tools Elton (for interaction data extraction), Preston (for reliable dataset tracking) and Nomer (for taxonomic name alignment). Nonetheless, the development of new vocabularies, standards and best practice guides would facilitate aggregation of interaction data, including the diversification of the GBIF data model (GBIF 2022b) for sharing biodiversity data beyond occurrences data. That is the aim of the TDWG Interest Group on Biological Interactions Data (TDWG 2022). 

   more » « less