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
Nowadays very large domain ontologies are being developed in life-science areas like Biomedicine, Agronomy, Astronomy, etc. Users and applications can benefit enormously from these ontologies in very different tasks, such as visualization, vocabulary homogenizing and data classification. However, due to their large size, they are often unmanageable for these applications. Instead, it is necessary to provide small and useful fragments of these ontologies so that the same tasks can be performed as if the whole ontology is being used. In this work we present a novel method for efficiently indexing and generating ontology fragments according to the user requirements. Moreover, the generated fragments preserve relevant inferences that can be made with the selected symbols in the original ontology. Such a method relies on an interval labeling scheme that efficiently manages the transitive relationships present in the ontologies. Additionally, we provide an interval's algebra to compute some logical operations over the ontology concepts. We have evaluated the proposed method over several well-known biomedical ontologies. Results show very good performance and scalability, demonstrating the applicability of the proposed method in real scenarios. (C) 2009 Elsevier Inc. All rights reserved.
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