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
Thanks to state-of-the-art technologies, we have more and more modern infrastructures as well as automatic processes supporting the agricultural domain. Data collected from parcels by these systems and remote sensors for further analysis result in facing the three main challenges which are known as big volume, big variety, and big velocity, in the era of big data. In terms of similarity search, we propose a range-based clustering method that finds objects which are the most similar compared to the given object in a large-scale computing with MapReduce. The proposed method groups objects into different clusters which are considered as pivots to perform pre-checking before computing similarity. Furthermore, we conduct some basic experiments to evaluate the performance of the proposed method and observe the influences of the clusters in similarity search.
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