TY - JOUR
AU - Elizabeth Arnaud
AU - Marie-Angélique Laporte
AU - Soonho Kim
AU - Céline Aubert
AU - Sabina Leonelli
AU - Berta Miro
AU - Laurel Cooper
AU - Pankaj Jaiswal
AU - Gideon Kruseman
AU - Rosemary Shrestha
AU - Pier Buttigieg
AU - Christopher Mungall
AU - Julian Pietragalla
AU - Afolabi Agbona
AU - Jacqueline Muliro
AU - Jeffrey Detras
AU - Vilma Hualla
AU - Abhishek Rathore
AU - Roma Das
AU - Ibnou Dieng
AU - Guillaume Bauchet
AU - Naama Menda
AU - Cyril Pommier
AU - Felix Shaw
AU - David Lyon
AU - Leroy Mwanzia
AU - Henry Juarez
AU - Enrico Bonaiuti
AU - Brian Chiputwa
AU - Olatunbosun Obileye
AU - Sandrine Auzoux
AU - Esther Yeumo
AU - Lukas Mueller
AU - Kevin Silverstein
AU - Alexandra Lafargue
AU - Erick Antezana
AU - Medha Devare
AU - Brian King
AB - <p>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.</p>

BT - Patterns (N Y)
DA - 2020 Oct 09
DO - 10.1016/j.patter.2020.100105
IS - 7
J2 - Patterns (N Y)
LA - eng
N2 - <p>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.</p>

PY - 2020
EP - 100105
T2 - Patterns (N Y)
TI - The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems.
VL - 1
SN - 2666-3899
ER -