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
In this paper, we propose a proficient method for knowledge retrieval in edaphology to assist the edaphologists and those who are related with agriculture in a big way. The proposed method mainly consists of two phases of which the first one is to build the knowledge base using XML and the latter part deals with information retrieval using fuzzy search. Initially, the relational database is converted to XML database. This paper discusses two algorithms, one is when the soil characteristics are given as input to have the plant list and in the other, plant names are given as input to have the soil characteristics suited for the plant. While retrieving the query result, the crisp numerical values are converted to fuzzy value using the triangular fuzzy membership function and matched to those in database. Those which satisfy are added to the result list and subsequently, the frequency is found out to rank the result list so as to obtain the final sorted list. Performances metrics are used in order to evaluate the method and compared to baseline paper to identify the number of plants retrieved, ranking efficiency, and computation time and memory usage. Results obtained proved the validity of the method and the method obtained the average computation time of 0.102 seconds and average memory usage of 2 486 Kb, which are all far better than our previous method results.
Inappropriate format for Document type, expected simple value but got array, please use list format