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
While in the past most information on the internet was generated by humans or computers, with the emergence of the Internet of Things, vast amount of data is now being created by sensors from devices, machines etc, which are placed in the physical world. Here we present a series of example applications enabled by such sensor data and what we call "Physical Analytics", which provides the underlying intelligence using a combination of physical and statistical models. The smarter solutions, which are being presented in this talk, range from active energy management and optimization, environmental sensing and controls, precision agriculture to renewable energy forecasting. All these different applications have been built using a single platform, which is comprised of a set of "configurable" technologies components including ultra-low power sensing and communication, big data management technologies, numerical modeling for physical systems, machine learning based physical model blending, and physical analytics based automation and control.
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