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
Generally, information retrieval (IR) performs keyword search based on the user query to find a set of relevant documents. In the domain of agricultural expertise retrieval, the goal is to find a group of experts who has knowledge in agriculture (by using publications as the evidence) specified by the input query. Typical publication IR systems could sometimes return the search result sets, which consist of a huge amount of publications. Some of the returned publications are not relevant to the individual user's information need. In this paper, an ontology based agricultural expertise retrieval framework called AGRIX is proposed with the focus on the ontology creation to cover three following aspects: (1) expert profiles and publications, (2) type of plants and (3) problem solving. To build the Ontology model, we used a set of publications (1,249 records) which was collected from the Thai national AGRIS center, Bureau of Library Kasetsart University. In addition, a set of inference rules is created to support the expertise retrieval task. By using AGRIX to implement an agricultural expertise retrieval, users can search for experts in two perspectives, plant (e.g., rice, sugar canes) and problem solving (e.g., plant diseases, fertilizers).
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