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
Soil moisture plays a significant role in both water and energy cycles. It is important to manage and analyze in situ sensor observations of soil moisture due to its impacts on agricultural and hydrological processes. Google Fusion Tables (GFT) is a cloud computing database that provides a service on the Web for data management and integration. Using GFT for managing soil moisture sensor observations, it is possible to take advantages of GFT for collaborative management, on-the-fly visualization, and flexible integration and analysis. The Open Geospatial Consortium (OGC) sensor observation service (SOS) can provide real-time or near-real-time observations in an interoperable way. Combing SOS and GFT together can take the best of both. The paper investigates how GFT could be employed for managing, visualizing, and analyzing soil moisture sensor observations. It describes the design and implementation of a cloud-based SOS for managing soil moisture data using cloud computing databases. By storing sensor observations in GFT, the SOS service is scalable, and observations can be visualized and analyzed on demand. Challenges and approaches on the integration of GFT and SOS are discussed. A prototype service on sharing and managing soil moisture sensor observations is developed to demonstrate the applicability of the approach.
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