A5026303816- Semantic Web and Ontologies
- Biomedical Text Mining and Ontologies
- Research Data Management Practices
- Botanical Research and Chemistry
- Building Energy and Comfort Optimization
- Seed and Plant Biochemistry
- Agriculture and Rural Development Research
- Genetics, Bioinformatics, and Biomedical Research
- Smart Agriculture and AI
- Genetics and Plant Breeding
- Scientific Computing and Data Management
Bioversity International
2020
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture agrifood systems requires quality labeling or annotation in order to be interoperable. As recommended the FAIR principles, data, labels, metadata must use controlled vocabularies ontologies that are popular knowledge domain commonly used community. Despite existence of robust Life Sciences, there is currently no comprehensive full set for across agricultural disciplines. In this paper, we discuss...
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture agrifood systems requires quality labelling to be interoperable. As recommended the FAIR principles, data, labels metadata must use controlled vocabularies ontologies that are popular in knowledge domain commonly used community. Despite existence of robust Life Sciences, there is currently no agreed full set for annotation across agricultural disciplines, which may span genetics, environment,...
Agricultural research has been traditionally driven by linear approaches dictated hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical are possible that operate over large pools to discern patterns. Such need contain well-described, machine-interpretable, and openly available (represented high-scoring Findable, Accessible, Interoperable, Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture developed several solutions help...