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
agriOpenLink: Towards Adaptive Agricultural Processes Enabled by Open Interfaces, Linked Data and Services
Today, users involved in agricultural production processes increasingly rely on advanced agricultural machines and specialized applications utilizing the latest advances in information and communication technology (ICT). Robots and machines host numerous specialized sensors and measurement devices and generate large amounts of data that combined with data coming from external sources, could provide a basis for better process understanding and process optimization. One serious roadblock to this vision is a lack of interoperability between the equipment of different vendors; another pitfall of current solutions is that the process knowledge is not modelled in a standardized machine readable form. On the other hand, such process model can be flexibly used to support process-specific integration of machines, and enable context-sensitive automatic process optimization. This paper presents an approach and preliminary results regarding architecture for adaptive optimization of agricultural processes via open interfaces, linked data and semantic services that is being developed within the project agriOpenLink; its goal is to provide a novel methodology and tools for semantic proces orchestraion and dynamic context-based adaptation, significantly reducing the effort needed to create new ICT-controlled agricultural applications involving machines and users.
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