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 search of classification that supports the dynamics of science: the FAO Land Cover Classification System and proposed modifications
Classification of geographic phenomena is often a black box to anyone outside the immediate group involved in the classification process. There is a growing need for compatibility between datasets to map, evaluate, and monitor areas in a consistent manner. The FAO (Food and Agriculture Organization of the United Nations) Land Cover Classification system (LCCS) is a proposed method to enable interoperability for land-cover data and an attempt to open the classification black box for scrutiny. The FAO LCCS is used to demonstrate some of the strengths and weaknesses of feature-based classification methods and how some important improvements, based on theoretical developments in geographic information science, can extend LCCS to become a 'boundary object' that supports representation, negotiation, and analysis of dynamic and heterogeneous classification systems. The suggested improvements also include an outline of how future classification activities could be developed into a distributed web-based ontology infrastructure.
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