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

Arnaud, Elizabeth; Laporte, Marie‐Angélique; Kim, Soonho; Aubert, Céline; Leonelli, Sabina; Miró, Berta; Cooper, Laurel; Jaiswal, Pankaj; Kruseman, Gideon; Shrestha, Rosemary; Buttigieg, Pier Luigi; Mungall, Christopher J.; Pietragalla, Julian; Agbona, Afolabi; Muliro, J.; Detras, Jeffrey; Hualla, Vilma; Rathore, Abhishek; Das, Roma Rani; Dieng, Ibnou; Bauchet, Guillaume; Menda, Naama; Pommier, Cyril; Shaw, Felix; Lyon, David; Mwanzia, Leroy; Juárez, Henry; Bonaiuti, Enrico; Chiputwa, Brian; Obileye, Olatunbosun; Auzoux, Sandrine; Yeumo, Esther Dzalé; Mueller, Lukas A.; Silverstein, Kevin A.T.; Lafargue, Alexandra; Antezana, Erick; Devare, Medha; King, B. R.

doi:10.1016/j.patter.2020.100105

Cited by 62 publications

(58 citation statements)

References 33 publications

(37 reference statements)

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“…Examples of large international repositories are given in Table 1. (Arnaud et al, 2020). A useful best practice for integrating data from different sources and repositories is the use of smart templates that can check for accuracy and validity of data inputs, and identify common variables that can be used to link multiple datasets.…”

Section: Using Data Repositoriesmentioning

confidence: 99%

Agricultural data management and sharing: Best practices and case study

et al. 2021

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Agricultural data are crucial to many aspects of production, commerce, and research involved in feeding the global community. However, in most agricultural research disciplines standard best practices for data management and publication do not exist. Here we propose a set of best practices in the areas of peer review, minimal dataset development, data repositories, citizen science initiatives, and support for best data management. We illustrate some of these best practices with a case study in dairy agroecosystems research. While many common, and increasingly disparate data management and publication practices are entrenched in agricultural disciplines, opportunities are readily available for promoting and adopting best practices that better enable and enhance data-intensive agricultural research and production.

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Section: Using Data Repositoriesmentioning

confidence: 99%

Agricultural data management and sharing: Best practices and case study

et al. 2021

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“…With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web [34], it becomes essential that such construction and evaluation methods are put forward to guide future efforts of ontology development. Such efforts are particularly important in the context of agriculture, which, as discussed above, covers a wide range of knowledge areas, concepts, sources and formats, and hence demands complex ontologies.…”

Section: Goalmentioning

confidence: 99%

“…This is not surprising, given that agriculture is a knowledge-centric field that covers many areas of expertise and many world-wide used practices and technologies. Agriculture also includes numerous concepts that are often designated by different names with similar meanings [20,33,34], fragmented across different systems [35]. Furthermore, the ability to integrate and harmonize large amounts of agricultural information, originating from a wide range of sources and in various formats, has been recently identified as a key perquisite for sustainable agriculture [36].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Evaluating Agricultural Ontologies

2021

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An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.

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“…The Crop Ontology used by breeding databases provides descriptions, URIs (unique identifiers) and relationships of agronomic, morphological, physiological, quality, and stress traits. It follows a conceptual model that defines a phenotypic variable as a combination of a trait, a method and a scale (Shrestha et al, 2012) (Shrestha et al, 2012;Arnaud et al, 2020). Therefore, the tricot ranking method needs to be included into the Crop Ontology for all traits that are relevant to the tricot trials.…”

Section: Data Standardization and Ontologiesmentioning

confidence: 99%

The tricot citizen science approach applied to on-farm variety evaluation: methodological progress and perspectives

Etten¹,

Abidin²,

Arnaud³

et al. 2020

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The tricot citizen science approach applied to on-farm variety evaluation: methodological progress and perspectives Published by the CGIAR Research Program on Roots, Tubers and BananasThe CGIAR Research Program on Roots, Tubers and Bananas (RTB) is a partnership collaboration led by the International Potato Center implemented jointly with the Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), the International Institute of Tropical Agriculture (IITA), and the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), that includes a growing number of research and development partners. RTB brings together research on its mandate crops: bananas and plantains, cassava, potato, sweetpotato, yams, and minor roots and tubers, to improve nutrition and food security and foster greater gender equity especially among some of the world's poorest and most vulnerable populations.

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

Cited by 62 publications

References 33 publications

Agricultural data management and sharing: Best practices and case study

Agricultural data management and sharing: Best practices and case study

A Framework for Evaluating Agricultural Ontologies

The tricot citizen science approach applied to on-farm variety evaluation: methodological progress and perspectives

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