A comprehensive comparison of automated FAIRness Evaluation Tools

The FAIR Guiding Principles (Findable, Accessible, Interop- erable, and Reusable) have been widely endorsed by the scientific community, funding agencies, and policymakers. However, the FAIR principles leave ample room for different implementations, and several groups have worked towards manual, semi-automatic, and automatic approaches to evaluate the FAIRness of digital objects. This study compares and con- trasts three automated FAIRness evaluation tools namely F-UJI, the FAIR Evaluator, and FAIR Checker. We examine three aspects: 1) tool characteristics, 2) the evaluation metrics, and 3) metrics tests for three public datasets. We find significant differences in the evaluation results for tested resources, along with differences in the design, implementation, and documentation of the evaluation metrics and platforms. While auto- mated tools do test a wide breadth of technical expectations of the FAIR principles, we put forward specific recommendations for their improved utility, transparency, and interpretability

Harnessing the power of unified metadata in an ontology repository: The case of AgroPortal

As any resources, ontologies, thesaurus, vocabularies and terminologies need to be described with relevant metadata to facilitate their identification, selection and reuse. For ontologies to be FAIR, there is a need for metadata authoring guidelines and for harmonization of existing metadata vocabularies—taken independently none of them can completely describe an ontology. Ontology libraries and repositories also have to play an important role. Indeed, some metadata properties are intrinsic to the ontology (name, license, description); other information, such as community feedbacks or relations to other ontologies are typically information that an ontology library shall capture, populate and consolidate to facilitate the processes of identifying and selecting the right ontology(ies) to use. We have studied ontology metadata practices by: (1) analyzing metadata annotations of 805 ontologies; (2) reviewing the most standard and relevant vocabularies (23 totals) currently available to describe metadata for ontologies (such as Dublin Core, Ontology Metadata Vocabulary, VoID, etc.); (3) comparing different metadata implementation in multiple ontology libraries or repositories. We have then built a new metadata model for our AgroPortal vocabulary and ontology repository, a platform dedicated to agronomy based on the NCBO BioPortal technology. AgroPortal now recognizes 346 properties from existing metadata vocabularies that could be used to describe different aspects of ontologies: intrinsic descriptions, people, date, relations, content, metrics, community, administration, and access. We use them to populate an internal model of 127 properties implemented in the portal and harmonized for all the ontologies. We—and AgroPortal's users—have spent a significant amount of time to edit and curate the metadata of the ontologies to offer a better synthetized and harmonized information and enable new ontology identification features. Our goal was also to facilitate the comprehension of the agronomical ontology landscape by displaying diagrams and charts about all the ontologies on the portal. We have evaluated our work with a user appreciation survey which confirms the new features are indeed relevant and helpful to ease the processes of identification and selection of ontologies. This paper presents how to harness the potential of a complete and unified metadata model with dedicated features in an ontology repository; however, the new AgroPortal's model is not a new vocabulary as it relies on preexisting ones. A generalization of this work is studied in a community-driven standardization effort in the context of the RDA Vocabulary and Semantic Services Interest Group

Transformation and integration of heterogeneous health data in a privacy-preserving distributed learning infrastructure

Problem statement: A growing volume and variety of personal health data are being collected by different entities, such as healthcare providers, insurance companies, and wearable device manufacturers. Combining heterogeneous health data offers unprecedented opportunities to augment our understanding of human health and disease. However, a major challenge to research lies in the difficulty of accessing and analyzing health data that are dispersed in their format (e.g. CSV, XML), sources (e.g., medical records, laboratory data), representation (unstructured, structured), and governance (e.g., data collection and maintenance)[2]. Such considerations are crucial when we link and use personal health data across multiple legal entities with different data governance and privacy concerns

Classification of pig farms regarding environmental risk and internal use of pig manure

224pThis paper presents the practices of Vietnamese farmers in handling animal excreta, i.e., pig manure, and highlights the difference of farms in terms of their operation relative to waste management and utilization. The environmental risks associated with these practices, and the attempt to quantify the difference of pork production systems, are also explored. Finally, a typology constructed based on the difference of practices and the quantification of risks is also given

SIFR BioPortal : Un portail ouvert et générique d’ontologies et de terminologies biomédicales françaises au service de l’annotation sémantique

National audienceContexte – Le volume de données en biomédecine ne cesse de croître. En dépit d'une large adoption de l'anglais, une quantité significative de ces données est en français. Dans le do-maine de l’intégration de données, les terminologies et les ontologies jouent un rôle central pour structurer les données biomédicales et les rendre interopérables. Cependant, outre l'existence de nombreuses ressources en anglais, il y a beaucoup moins d'ontologies en français et il manque crucialement d'outils et de services pour les exploiter. Cette lacune contraste avec le montant considérable de données biomédicales produites en français, par-ticulièrement dans le monde clinique (e.g., dossiers médicaux électroniques). Methode & Résultats – Dans cet article, nous présentons certains résultats du projet In-dexation sémantique de ressources biomédicales francophones (SIFR), en particulier le SIFR BioPortal, une plateforme ouverte et générique pour l’hébergement d’ontologies et de terminologies biomédicales françaises, basée sur la technologie du National Center for Biomedical Ontology. Le portail facilite l’usage et la diffusion des ontologies du domaine en offrant un ensemble de services (recherche, alignements, métadonnées, versionnement, vi-sualisation, recommandation) y inclus pour l’annotation sémantique. En effet, le SIFR An-notator est un outil d’annotation basé sur les ontologies pour traiter des données textuelles en français. Une évaluation préliminaire, montre que le service web obtient des résultats équivalents à ceux reportés précedement, tout en étant public, fonctionnel et tourné vers les standards du web sémantique. Nous présentons également de nouvelles fonctionnalités pour les services à base d’ontologies pour l’anglais et le français