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 volumetric aspect of big data is increasing because of introduction of new methods to generate-capture data and the diverse needs of that data. Agro-meteorological maps (e.g. digital maps, printed maps, scanned maps etc.) and satellite imagery are also sources of big data in terms of volume, variety in format, scale, representation, then presence of noise resulting in veracity and the velocity in terms of rate of availability of satellite imagery. One main problem with these domain specific agro-meteorological maps is presence of veracity in terms of noise (i.e. irrelevant data in maps) such as, city names, symbols, administrative boundaries etc. If we can successfully remove noise from these maps and replace with right data, we can convert these maps into rich sources of digital data resulting in new opportunities in traditional agricultural sector, in policy making and decision_support. In this paper we present an approach and its implementation that can be used to minimize the veracity in maps by cleaning the noise from maps and replacing the noise with right data. Once maps are cleaned, right data can be extracted from them. This data can be used by different big data applications and knowledge based systems in decision and policy making in different sectors.
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