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 discovery in spatial databases represents a particular case of discovery, allowing the discovery of relationships that exist between spatial and non-spatial data. Spatial reasoning ought to play a very important role in spatial data mining, but the research combined SR and SDM are very few. This paper describes the conception and implementation of SRSDM, the tool for data mining in spatial databases based on spatial reasoning method. Most spatial data mining systems only support topological relation, nearly all previous GIS and AI researches focused on single spatial aspect. Those were quite inadequate for practical applications. We propose a new spatial knowledge representation which integrates topology, direction, distance and size relations. SRSDM includes three parts: extracting spatial relations, frameworks for traditional or new data mining algorithms.
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