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
Services in ubiquitous agricultural environments could be more elaborate and autonomous by intercommunicating with various sensors and systems, based on the service architecture of the device-to-device or device-to-service. To do that, the situation information that sensors gain from agricultural environments should be recomposed into contexts, which can be understood or recognized by services and devices. In this paper, we propose an OWL-based context model for agricultural environments, which can be used as service execution conditions for various context-aware service applications based on ubiquitous sensor networks in u-agriculture. The proposed context model is based on OWL ontology, which can be easily and efficiently customized in the various fields of agricultural service domains. With the proposed context model, developers can easily implement context-aware agricultural services through idiomatic communication with device-to-device, sensor-to-sensor, or sensor-to-service in agricultural environments. Especially, we hope that the proposed context model can be greatly helpful in the developments of smart agricultural services in the cultivation environments equipped with various sensors such as greenhouse, glasshouse, and vertical farm and so on.
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