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
This paper presents an engineering experience for building a Semantic Web compliant system for a scientific application - agricultural zoning. First, we define the concept of ontological cover and a set of relationships between such covers. These definitions, based on domain ontologies, can be used, for example, to support the discovery of services on the Web. Second, we propose a semantic acyclic restriction on ontologies which enables the efficient comparison of ontological covers. Third, we present different engineering solutions to build ontology views satisfying the acyclic restriction in a prototype. Our experimental results unveil some limitations of the current Semantic Web technology to handle large data volumes, and show that the combination of such technology with traditional data management techniques is an effective way be achieve highly functional and scalable solutions.
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