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
Nowadays, the use of Internet of Things (IoT) makes more and more devices be capable of producing large amount of data over a short period of time. Sensors started to be used more often which makes them become one of the main sources of structured data that is measured every minute for dozens of environmental parameters, as well as one of the main problems faced by the systems that store, process and analyze this large amount of information. Using IoT there were implemented solutions which imply monitoring different types of environments. Smart cities analyze the measured values for specific parameters to optimize the life quality of the population, smart farms process the data reported by sensors to make an efficient use of resources and traffic applications rely on the collected data from the drivers' smartphones to avoid traffic congestion. This paper describes a solution for large scale time series visualization, offering a generic architecture that can be used in any system that works with structured data and shows how this architecture can be used in a certain system like Smart farms. We present a Cloud-based solution for time series data gathering, the workflow of visualization services and the integration with Farm History and Farm Analytics applications. The performance evaluation of proposed services proves that the solution makes an efficient analysis of data, using both raw data and statistics, depending on the type of visualization.
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