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 a previous paper we described a machine learning approach which was used to automatically generate food-webs from national-scale agricultural data. The learned food-webs in the previous study consist of hundreds of ground facts representing trophic links between individual species. These species food-webs can be used to explain the structure and dynamics of particular eco-systems, however, they cannot be directly used as general predictive models. In this paper we describe the first steps towards this generalisation and present initial results on (i) learning general functional food-webs (i.e. trophic links between functional groups of species) and (ii) meta-interpretive learning (MIL) of general predictive rules (e.g. about the effect of agricultural management). Experimental results suggest that functional food-webs have at least the same levels of predictive accuracies as species food-webs despite being much more compact. In this paper we also present initial experiments where predicate invention and recursive rule learning in MIL are used to learn food-webs as well as predictive rules directly from data.
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