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
An Improved Ant Colony Algorithm for Agricultural Knowledge Storage Scheduling Under Grid Environment
Distributed knowledge resources management is one of the key technologies for the development of grid computing as well as cloud computing. In this paper, an improved Ant Colony Algorithm is proposed to apply to agriculture knowledge resources scheduling under grid environment. Instead of the random and uncertain of grid nodes distances under Ant Colony Algorithm, the improved Ant Colony Algorithm expressed the distances of grid nodes directly using two-dimensional coordinate system according to the semantic distances of domain concepts. The distances of different kinds of agriculture knowledge resources can be calculated visualized. The efficiency of grid storage scheduling process can be improved normally more than 5 times comparing to the traditional Ant Colony Algorithm.
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