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
The coordinated learning is importance of technique for cooperative multi-objects system in large-scale Internet of Things . The coordinated learning has attracted a lot of attention for its applications in Internet of Things. However, the self-adaptive makes the coordinated learning difficult to be used in IoT. This paper proposes multi-objects scalable coordinated learning algorithm based on the maximum potential loss of coordination. The algorithm defines an interaction measure that allows objects to dynamically estimate the potential utility loss of coordination with any cluster of objects. The interaction mechanism makes each object compute their beneficial coordination set in different situations and makes the best use of their limited communication resource in Internet of Things. As a result of experiments, our algorithm adapts policy learning of object and their coordination network for different context. Finally, the experiments with the smart agriculture data set demonstrate that the proposed scheme is effective and robust.
Inappropriate format for Document type, expected simple value but got array, please use list format