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
In recent years, the evolving wireless technology, cheaper micro-controllers, smart cities concept and 'Internet of Things' (IoTs) have given way to the need of online wireless management systems for smart weather stations (SWS). In this paper, we develop an 'Online Smart Weather Station System' for studying the correlation amongst multiple weather parameters data, collected over a period of 18 months. The system consists of sensors that generate weather data, local storage, wireless transfer to control centre, and web-based online representation and analysis of data. Six weather parameters such as air temperature, relative humidity, wind-speed, rain, rain-rate and solar energy are studied. Statistical measures such as mean, median, standard deviation, min, max and normalized standard deviation are computed from the weather data. The correlation among temperature, humidity and wind-speed parameters is presented. Few important observations are made, for possible applications in agriculture, construction and manufacturing activities. Further a web-based service can be aimed at automated analysis of weather data generated by the smart weather station.
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