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
The increase of the amount of agricultural remote sensing monitoring data makes it difficult for data storage and management thereby limiting the utilization of data resources. Considering the security and response time, the original data cannot be directly exposed on the Internet for user queries. Therefore, there is an urgent need to organize and describe agricultural remote sensing monitoring data effectively for users to understand and query. In this paper, based on a detailed analysis, for rational planning and organizing of agricultural remote sensing monitoring data resources, a M-A (Metadata of Agricultural Remote Sensing Monitoring Data) model is constructed with the study of data characteristics and the Grid environment. The M-A model structure and its contents are designed using the XML language which gives a relatively comprehensive description of agricultural remote sensing monitoring data and the Grid environment. In summary, the study of this paper provides a practical and effective support for data standardization, sharing, exchanging and integration under the Grid environment. (C) 2010 Elsevier Ltd. All rights reserved.
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