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
From November 2010 to April 2011, our country declared a state of national disaster, which was an upgrade from the serious warning that was released because of foot-and-mouth cattle disease outbreak. The number of breeding cattle was 3,344, which was a 5.7% increase from that in the previous year. Therefore, it was imperative to respond immediately and alter the situation through frequent clinical observations and monitoring to block disease progression by quick forecasting prior to outbreak, rather than employing extreme preventive methods such as destruction of livestock after the outbreak. In this study, ontology is used to express standardized status information on cattle disease, define the relationships of status information by using standardized ontology language, Web Ontology Language (OWL), and execute efficient and intelligent cattle disease forecasting services by deducing cattle disease using Semantic Web Rule Language (SWRL).
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