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
Predicting shellfish farm closures using time series classification for aquaculture decision_support
Closing a shellfish farm due to pollutants usually after high rainfall and hence high river flow is an important activity for health authorities and aquaculture industries. Towards this problem, a novel application of time series classification to predict shellfish farm closure for aquaculture decision_support is investigated in this research. We exploit feature extraction methods to identify characteristics of both univariate and multivariate time series to predict closing or re-opening of shellfish farms. For univariate time series of rainfall, auto-correlation function and piecewise aggregate approximation feature extraction methods are used. In multivariate time series of rainfall and river flow, we consider features derived using cross-correlation and principal component analysis functions. Experimental studies show that time series without any feature extraction methods give poor accuracy of predicting closure. Feature extraction from rainfall time series using piecewise aggregate approximation and auto-correlation functions improve up to 30% accuracy of prediction over no feature extraction when a support vector machine based classifier is applied. Features extracted from rainfall and river flow using cross-correlation and principal component analysis functions also improve accuracy up to 25% over no feature extraction when a support vector machine technique is used. Overall, statistical features using auto-correlation and cross-correlation functions achieve promising results for univariate and multivariate time series respectively using a support vector machine classifier. (C) 2014 Elsevier B.V. All rights reserved.
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