The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Several current research projects are focused on the creation of haplotype maps to identify and describe common genetic variation in some species. Studies on haplotype maps are key in understanding how natural selection has produced genomic differences between subspecies of a given species. Important insight can be obtained by determining which variations in the genotype are associated with important phenotypical differences between individuals. Pattern recognition theory and machine learning techniques are useful tools to reveal this connection from a large amount of data provided by haplotype maps. In this work, we applied discrete classifiers and feature selection techniques for the prediction of cattle coat color from genotypes. We compared the performance of different classification rules and showed the feasibility of this approach for the prediction of phenotype based on genotype.
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