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
Most of expert knowledge in agriculture is descriptive and experiential, so it is difficult to describe in mathematics and build decision_support system (DSS) for greenhouse. Therefore, the decision_support system (DSS) for greenhouse constructed of data warehouse and date mining technology was introduced in this paper. In the system, data warehouse was founded to memory diversified date, the using of on-line analytical processing and date mining enriches knowledge base with new agriculture information. Implementation of system adopted SQL Server analysis, as a result, tightness coupling of data warehouse, date mining and application, improved efficiency of date mining. Combined data warehouse with on-line analytical processing and date mining to construct a novel DSS.
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