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
Computational model, measurement infrastructure, and information technology are currently used to analyze and predict the characteristics and behaviors of complex systems. Most of the computational models used to date, however, only allow data inputs that are fixed when the simulations are launched. These simulation and measurement approaches are serialized and static but not synchronized and cooperative. The lack of capability to simultaneously inject measured data into simulation models limits the dynamic requirements for simulations in response to the real-time changing conditions and therefore is unable to catch the instantaneous reactions and occurrences in nature. Dynamic data driven application system (DDDAS) is a new paradigm in which simulations, measurements, and applications are dynamically integrated, creating new capabilities for a wide range of science and engineering applications. It is a symbiotic feedback control system, which can dynamically employ simulations to guide experimental measurements and to determine when, where, and how to gather additional data, and in reverse, can dynamically steer the simulations based on the experimental measurements, and thereby promising more accurate and precise analyses and predictions. This study presents an overview on recent development and future perspective of the DDDAS. The basic concept and examples of the DDDAS applied to general science, natural sciences, and engineering are given. Suggested areas on implementations of the DDDAS and its limitations are discussed. Synthesis of literature reveals that the DDDAS has great application potentials in many aspects of environmental, agricultural, and ecological practices. (C) 2006 Elsevier B.V. All rights reserved
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