Developing data interoperability using standards: A wheat community use case

Yeumo, Esther Dzalé; Alaux, Michaël; Arnaud, Elizabeth; Aubin, Sophie; Baumann, Ute; Buche, Patrice; Cooper, Laurel; Ćwiek‐Kupczyńska, Hanna; Davey, Robert P.; Fulss, Richard; Jonquet, Clément; Laporte, Marie‐Angélique; Larmande, Pierre; Pommier, Cyril; Protonotarios, Vassilis; Reverté, Carmen; Shrestha, Rosemary; Subirats, Imma; Venkatesan, Aravind; Whan, Alex; Quesneville, Hadi

doi:10.12688/f1000research.12234.2

Cited by 19 publications

(4 citation statements)

References 9 publications

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“…However, the diversity of data type and the concomitant lack of data harmonisation and standards hamper cross-referencing and meta-analysis. A joint action between the WheatIS EWG and a group of linked data scientists created the Wheat Data Interoperability Working Group under the Research Data Alliance (RDA) umbrella [45] to help tackle this difficult issue [46]. The Wheat@URGI portal continuously evolves its repository to follow the standard recommendations [47].…”

Section: Discussionmentioning

confidence: 99%

Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data

Alaux

Rogers²,

Letellier

et al. 2018

Preprint

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26The Wheat@URGI portal (https://wheat-urgi.versailles.inra.fr) has been developed to provide 27 the international community of researchers and breeders with access to the bread wheat 28 reference genome sequence produced by the International Wheat Genome Sequencing 29 Consortium. Genome browsers, BLAST, and InterMine tools have been established for in 30 depth exploration of the genome sequence together with additional linked datasets including 31 physical maps, sequence variations, gene expression, and genetic and phenomic data from 32 other international collaborative projects already stored in the GnpIS information system. The 33 portal provides enhanced search and browser features that will facilitate the deployment of 34 the latest genomics resources in wheat improvement. 35 36 37 Background 38The International Wheat Genome Sequencing Consortium (IWGSC) [1] is an international 39 collaborative group of growers, academic scientists, and public and private breeders that was 40 established to generate a high quality reference genome sequence of the hexaploid bread 41 wheat, and to provide breeders with state-of-the-art tools for wheat improvement. The vision 42 of the consortium is that the high quality, annotated ordered genome sequence integrated 43 with physical maps will serve as a foundation for the accelerated development of improved 44 varieties and will empower all aspects of basic and applied wheat science to address the 45 important challenge of food security. A first analysis of the reference sequence produced by 46 the consortium (IWGSC RefSeq v1.0) was recently published [2]. 47To ensure that wheat breeding and research programs can make the most of this extensive 48 genomic resource, the IWGSC endorsed the establishment of a data repository at URGI (Unité parties in data management as well as analysis and usage of the sequence data. Wheat 54 functional genomics (expression, methylation, etc.), genetic, and phenomic data has increased 55 concurrently, requiring the development of additional tools and resources to integrate 56 different data for biologists and breeders. To manage this escalation of data, URGI have built 57 this data repository for the wheat community with the following specific aims: (i) store 58 resources for which no public archive exists (e.g. physical maps, phenotype information); (ii) 59 enable pre-publication access to specific datasets (e.g. sequence assemblies and annotations, 60 physical maps, markers); and (iii) rapid release of integrated resources upon publication. The 61 repository has been designed in accordance with the "FAIR" principles [3] to ensure that the 62 data are Findable, Accessible, Interoperable and Reusable. To address the challenge of 63 integrating diverse data types from multiple sources, URGI employs solutions that provide 64 enhanced features for data exploration, mining and visualisation using the GnpIS information 65 system [4] combined with a high level of data interoperability. 66Here we describe the data and tools currently available through...

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Section: Discussionmentioning

confidence: 99%

Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data

Alaux

Rogers²,

Letellier

et al. 2018

Preprint

Self Cite

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“…Among the first feedbacks and requirements of new users were the ability to describe ontology metadata with additional fields that what BioPortal originally provided. For instance, the RDA Wheat Data Interoperability (WDI) working group (http://ist.blogs.inra.fr/wdi) recommendations [24] pointed to AgroPortal to find standard wheat-related ontologies, but they needed licensing and access rights information to be more explicit and consistent. The group also required that the endorsement of the WDI for certain ontologies shall be made explicit on AgroPortal, in order to encourage the reuse of some specific ontologies.…”

Section: Motivating Use Casesmentioning

confidence: 99%

“…Knowledge organization systems types are taken from the NKOS Types Vocabulary of the Dublin Core initiative. 24 Natural languages are taken from the LEXVO vocabulary [66]. Ontology syntax values are provided by the W3C.…”

Section: Implementation Within Agroportalmentioning

confidence: 99%

Harnessing the Power of Unified Metadata in an Ontology Repository: The Case of AgroPortal

et al. 2018

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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.

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“…In 2013, the Interest Group on Agricultural Data (IGAD) ( https://www.rd-alliance.org/groups/agriculture-data-interest-group-igad.html ) was created within the Research Data Alliance to facilitate discussions on all aspects of agricultural information management. IGAD's Wheat Data Interoperability Working Group published guidelines recommending a set of standards and ontologies applicable to genetic, genomic, and phenotypic data ( http://datastandards.wheatis.org ) for wheat, 5 while its Agrisemantics Working Group conducted a scoping study from which it produced list of global recommendations for the development, maintenance, and use of semantic resources in agriculture ( https://rd-alliance.org/group/agrisemantics-wg/outcomes/39-hints-facilitate-use-semantics-data-agriculture-and-nutrition ). IGAD does not directly engage in ontology development related to agriculture.…”

Section: Introductionmentioning

confidence: 99%

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

Arnaud¹,

Laporte²,

Kim

et al. 2020

Patterns

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Summary Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

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Developing data interoperability using standards: A wheat community use case

Cited by 19 publications

References 9 publications

Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data

Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data

Harnessing the Power of Unified Metadata in an Ontology Repository: The Case of AgroPortal

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

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