The ontologies community of practice: a CGIAR initiative for big data in agrifood systems

The Bigger Picture Digital technology use in agriculture and agrifood systems research accelerates the production of multidisciplinary data, which spans genetics, environment, agroecology, biology, and socio-economics. Quality labeling of data secures its online findability, reusability, interoperability, and reliable interpretation, through controlled vocabularies organized into meaningful and computer-readable knowledge domains called ontologies. There is currently no full set of recommended ontologies for agricultural research, so data scientists, data managers, and database developers struggle to find validated terminology. The Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture harnesses international expertise in knowledge representation and ontology development to produce missing ontologies, identifies best practices, and guides data labeling by teams managing multidisciplinary information platforms to release the FAIR data underpinning the evidence of research impact. 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.

Bibliographic Details

Main Authors:	Arnaud, E., Laporte, Marie-Angélique, Kim, S., Aubert, C., Leonelli, S., Miro, B., Cooper, L., Jaiswal, P., Kruseman, Gideon K., Shrestha, R., Buttigieg, Pier Luigi, Mungall, C.J., Pietragalla, J., Agbona, A., Muliro, J., Detras, J., Hualla, V., Rathore, A., Das, R.R., Dieng, I., Bauchet, G.J., Menda, N., Pommier, C., Shaw, F., Lyon, D., Mwanzia, Leroy, Juarez, H., Bonaiuti, E., Chiputwa, B., Obileye, O., Auzoux, S., Yeumo, E.D., Mueller, L., Silverstein, K., Lafargue, A., Antezana, E., Devare, M., King, B.
Format:	Journal Article biblioteca
Language:	English
Published:	Elsevier 2020-10
Subjects:	ontology, agriculture, agrifood systems, crop production, breeding, phenotypes, genotypes,
Online Access:	https://hdl.handle.net/10568/113392 https://doi.org/10.1016/j.patter.2020.100105
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