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
Shellfish farms are needed to be closed if they are contaminated during their production as otherwise it may lead to serious health hazard. The authorities monitor a number of water quality variables to check the health of shellfish farms and to decide on the closure of the farms. The research presented in this paper aims to automate this process by developing novel algorithms to identify the cause of closure and also predicting the closure. As the frequency of closure is relatively very small, the labelled data sets are imbalanced in nature. We have developed a novel ensemble feature ranking algorithm that explicitly deals with class imbalance problem and identifies the cause of closure. We have also presented a class balancing ensemble classifier to predict shellfish farm closure. The class balancing ensemble classifier predicts closure/opening with as high as 71.69% accuracy and achieves best balancing act with decision tree base classifier in 75% locations. Rain and salinity are found to be the key causes of closure and the causality depends of the properties of the locations. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.
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