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
Ontology Based Data Access and Integration for Improving the Effectiveness of Farming in Nepal
It is widely accepted that food supply and quality are major problems in the 21st century. Due to the growth of the world's population, there is a pressing need to improve the productivity of agricultural crops, which hinges on different factors such as geographical location, soil type, weather condition and particular attributes of the crops to plant. In many regions of the world, information about those factors is not readily accessible and dispersed across a multitude of different sources. One of those regions is Nepal, in which the lack of access to this knowledge poses a significant burden for agricultural planning and decision making. Making such knowledge more accessible can boot up a farmer's living standard and increase their competitiveness on national and global markets. In this article, we show how we converted several available, although not easily accessible, datasets to RDF, thereby lowering the barrier for data re-usage and integration. We describe the conversion, linking, and publication process as well as use cases, which can be implemented using the farming datasets in Nepal.
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