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
Characterizing attributes of a society is fundamental to human geography. Cultural, social, and economic factors that are critical to understanding societal attitudes are associated with specific phenomena that are observable from overhead imagery. The application of remote sensing to specific issues, such as population estimation, agricultural analysis, and environmental monitoring, has shown great promise. Extending these concepts, we explore the potential for assessing aspects of governance, well-being, and social capital. Social science theory indicates the relationships among physical structures, institutional features, and social structures. Motivated by this underlying theory, we explore the relationship between observable physical phenomena and attributes of the society. Using imagery data from two study regions: sub-Saharan Africa and rural Afghanistan, we present an initial exploration of the direct and indirect indicators derived from the imagery. We demonstrate a methodology for extracting relevant measures from the imagery, using a combination of human-guided and machine learning methods. Our comparison of results for the two regions demonstrates the degree to which methods can generalize or must be tailored to a specific study area.
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