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
Cloud computing technologies can support high-performance geospatial services in various domains, such as smart city and agriculture. Apache Hadoop, an open-source software framework, can be used to build a cloud environment on commodity clusters for storage and large-scale processing of data sets. The Open Geospatial Consortium (OGC) Web 3-D Service (W3DS) is a portrayal service for three-dimensional (3-D) geospatial data. Its performance could be improved by cloud computing technologies. This paper investigates how OGC W3DS could be developed in a cloud computing environment. It adopts the Apache Hadoop as the framework to provide a cloud implementation. The design and implementation of the 3-D geospatial information cloud service is presented. The performance evaluation is performed over data retrieval tests running in a cloud platform built by Hadoop clusters. The evaluation results provide a valuable reference on providing high-performance 3-D geospatial information cloud services. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
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