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
It has been the irresistible trend of agriculture informationization which uses information technologies to treat enormous data and finds out potential useful rules to direct the development and reformation of agriculture. Aiming at the specific application of maize seed breeding, this paper effectively integrates several data mining technologies and presents a new method called CA to analyze the whole maize information. The algorithm achieves transverse dimension reduction by combining PCA and other methods, and also achieves longitudinal dimension reduction by improving CURE and k-means. The decision-tree method of CA algorithm introduces three different classifiers in order to enhance the accuracy of trees. By comparing the results of improved algorithm with traditional methods, we can find that the new algorithm is better in performance and degree of parallelism.
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