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
This paper proposes a cloud-based livestock disease monitoring system using wireless sensor networks, which aimed at minimizing damage caused by livestock disease and immediately responding to occurrence of stock disease. The proposed system collects a variety of livestock data, such as livestock activity and barn environment, from barns over wireless sensor networks, saves and manages the data with the use of the column-oriented database Hadoop HBase, a column-oriented database management system, and provides livestock disease forecasting and monitoring service through MapReduce model-based parallel data processing. In addition, it provides REST-based web service so that users can receive the service on various platforms, such as PCs or mobile devices. For verification of the system, we conducted challenge test in which the proposed system was applied to an actual barn, and then livestock disease was injected to measure livestock activity. The test result revealed that the proposed system was effective in finding a reduction in livestock activity, caused by livestock disease.
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