RESUMO
The agricultural industry and regulatory organizations define strategies and build tools and products for plant protection against pests. To identify different plants and their related pests and avoid inconsistencies between such organizations, an agreed and shared classification is necessary. In this regard, the European and Mediterranean Plant Protection Organization (EPPO) has been working on defining and maintaining a harmonized coding system (EPPO codes). EPPO codes are an easy way of referring to a specific organism by means of short 5 or 6 letter codes instead of long scientific names or ambiguous common names. EPPO codes are freely available in different formats through the EPPO Global Database platform and are implemented as a worldwide standard and used among scientists and experts in both industry and regulatory organizations. One of the large companies that adopted such codes is BASF, which uses them mainly in research and development to build their crop protection and seeds products. However, extracting the information is limited by fixed API calls or files that require additional processing steps. Facing these issues makes it difficult to use the available information flexibly, infer new data connections, or enrich it with external data sources. To overcome such limitations, BASF has developed an internal EPPO ontology to represent the list of codes provided by the EPPO Global Database as well as the regulatory categorization and relationship among them. This paper presents the development process of this ontology along with its enrichment process, which allows the reuse of relevant information available in an external knowledge source such as the NCBI Taxon. In addition, this paper describes the use and adoption of the EPPO ontology within the BASF's Agricultural Solutions division and the lessons learned during this work.
RESUMO
BACKGROUND AND OBJECTIVE: Metrics are commonly used by biomedical researchers and practitioners to measure and evaluate properties of individuals, instruments, models, methods, or datasets. Due to the lack of a standardized validation procedure for a metric, it is assumed that if a metric is appropriate for analyzing a dataset in a certain domain, then it will be appropriate for other datasets in the same domain. However, such generalizability cannot be taken for granted, since the behavior of a metric can vary in different scenarios. The study of such behavior of a metric is the objective of this paper, since it would allow for assessing its reliability before drawing any conclusion about biomedical datasets. METHODS: We present a method to support in evaluating the behavior of quantitative metrics on datasets. Our approach assesses a metric by using clustering-based data analysis, and enhancing the decision-making process in the optimal classification. Our method assesses the metrics by applying two important criteria of the unsupervised classification validation that are calculated on the clusterings generated by the metric, namely stability and goodness of the clusters. The application of our method is facilitated to biomedical researchers by our evaluomeR tool. RESULTS: The analytical power of our methods is shown in the results of the application of our method to analyze (1) the behavior of the impact factor metric for a series of journal categories; (2) which structural metrics provide a better partitioning of the content of a repository of biomedical ontologies, and (3) the heterogeneity sources in effect size metrics of biomedical primary studies. CONCLUSIONS: The use of statistical properties such as stability and goodness of classifications allows for a useful analysis of the behavior of quantitative metrics, which can be used for supporting decisions about which metrics to apply on a certain dataset.
