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
Identifying potential pollution sources in river basin via water quality reasoning based expert system
Indentifying potential pollution sources from the monitoring water quality data is desirable for water quality management. A simple expert system module was developed in this paper to infer the latent pollution sources and explain variation in water quality parameters. It contains three parts. The first part is water quality mining and analyzing component implemented by multivariate statistical techniques such as cluster analysis and factor analysis. The second part is knowledge base constructed by 53 emission standards for water pollutant of particular industries. The last part is the inference mechanism designed through calculating the similarity between a specific pollution factor and these particular industry emission standards. The Songhua River Harbin Region case study demonstrated the goodness of the expert system module. Three main pollution source factors in low pollution area were mainly related to organic pollution and nutrients (some point industry or animal husbandry and agriculture activities), heavy metal pollution (point sources: industries) and toxic pollution (point sources: pharmaceutical industries). The module will help managers make decisions with more strong confidence to improve the water quality.
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