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
Knowledge acquisition is the bottleneck of expert system. To solve this problem, KD (D&K), which is a comprehensive knowledge discovery process model cooperating both database and knowledge base, and related technology are proposed. Then based on KD (D&K) and related technology, the new construction of Expert System based on Knowledge Discovery (ESKD) is proposed. As the key knowledge acquisition component of ESKD, KD (D&K) is composed of KDD and KDK . KDD -the new process model based on double bases cooperating mechanism; KDK -the new process model based on double-basis fusion mechanism are introduced, respectively. The overall framework of ESKD is proposed. Some sub-systems and dynamic knowledge base system are discussed. Finally, the effectiveness and advantages of ESKD are tested in a real-world agriculture database. We hope that ESKD may be useful for the new generation of expert systems.
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