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
It is inherent to food supply chain networks that performance deviations occur occasionally due to variations in product quality and quantity. To reduce losses, one wants to be informed about such deviations as soon as possible, preferably even before they occur. Then it is possible to take actions to prevent or reduce negative consequences. In practice, extensive operational data is recorded, forming a valuable source for early warning and proactive control systems, i.e. decision_support systems for prediction and prevention of such performance problems. Data mining methods are ideal for analyzing such data sources and extracting useable information from them. In this paper, we present a novel framework for early warning and proactive control systems that combine expert knowledge and data mining methods to exploit recorded data. We discuss the implementation of a prototype system and the experiences from a case study regarding the applicability of the framework. (C) 2010 Published by Elsevier B.V.
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