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Shrestha, R.Senger, M.Ramil, M.Davenport, G.Arnaud, Elizabeth[Development of GCP Ontology for sharing crop information]Development of GCP Ontology for sharing crop information

CGIAR Platform for Big Data in Agriculture[BIG DATA Ontologies Community of Practice – Work Plan 2017]BIG DATA Ontologies Community of Practice – Work Plan 2017

Logical definitions, in particular those following the Entity-Quality approach, are increasingly used to drive automated classification of phenotypes and integrate phenotypes across species semantically. Over the years, the lack of consistent and widespread use of common standards resulted in con...


Matentzoglu, N.Balhoff, J.Bello, S.Boerkoel, C.Bradford, Y.Carmody, L.Cooper, L.Grove, C.Harris, N.Köhler, S.Laporte, M-A.Laulederkind, S.Lee, R.Mazandu, G.McMurry, J.Mungall, C.Osumi-Sutherland, D.Pilgrim, C.Rageth, K.Robb, S.Robinson, P.Segerdell, E.Thessen, A.Vasilevsky, N.Zhang, X.Haendel, M.[Phenotype Ontologies Traversing All The Organisms (POTATO) workshop aims to reconcile logical definitions across species. Workshop Report]Phenotype Ontologies Traversing All The Organisms (POTATO) workshop aims to reconcile logical definitions across species. Workshop Report

Assembling large-scale phenotypic datasets for evolutionary and biodiversity studies of plants can be extremely difficult and time consuming. New semi-automated Natural Language Processing (NLP) pipelines can extract phenotypic data from taxonomic descriptions, and their performance can be enhanc...


Endara L.Burleigh G.Cooper L.Jaiswal, P.Laporte, M-A.[A Natural Language Processing Pipeline to extract phenotypic data from formal taxonomic descriptions with a focus on flagellate plants]A Natural Language Processing Pipeline to extract phenotypic data from formal taxonomic descriptions with a focus on flagellate plants

Kulakow, P.A.Bakare, M.A.Shrestha, R.Arnaud, Elizabeth[Expansion of the crop ontology by adding cassava trait ontology]Expansion of the crop ontology by adding cassava trait ontology

CGIAR Platform for Big Data in Agriculture[BIG DATA Ontologies Community of Practice – Work Plan 2019]BIG DATA Ontologies Community of Practice – Work Plan 2019

CGIAR Platform for Big Data in Agriculture[BIG DATA Ontologies Community of Practice – Work Plan 2018]BIG DATA Ontologies Community of Practice – Work Plan 2018

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We ...


Venkatesan, A.Tagny Ngompe, G.El Hassouni, N.Chentli, I.Guignon, V.Jonquet, C.Ruiz, M.Larmande, P.[Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy]Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy
Agbona, A.Kulakow, P.A.Rabbi, Ismail Y.Prasad, P.Arnaud, ElizabethValette, L.Angelique, M.Mueller, L.Menda, N.[Cassavabase (cassavabase.org): an integrated field breeding and genomics database enables accelerated genetic gain in cassava]Cassavabase (cassavabase.org): an integrated field breeding and genomics database enables accelerated genetic gain in cassava

CGIAR Platform for Big Data in Agriculture[Decoding the Data Ecosystem: CGIAR Platform for Big Data in Agriculture 2018 Convention Report]Decoding the Data Ecosystem: CGIAR Platform for Big Data in Agriculture 2018 Convention Report

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