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
A key paradigm shift resulting from the intersection of the information technology (IT) and utility sectors is the availability of real-time data regarding energy use across different industries. Historically, ascertaining the energy costs across the value chain of a given product or service was a laborious and expensive task, requiring many months of data collection; several proxies or approximations for cases where measured data might not be cost-effectively available; and even then, the resulting energy footprint could have significant uncertainty based on time-of-measurement, geographic diversity of manufacturing sites, etc. As dynamic energy pricing begins to take hold and environmental externalities begin to be priced into existing cost structures, the ability to optimize a given value chain for minimal energy use becomes increasingly attractive. In this paper, we discuss an approach for leveraging dynamically available data alongside historical n-tier supply chain models to avail the ability for such optimization. The approach is illustrated for the case study of a computer manufacturer, where we find that metering electricity use at a small subset of sites can allow for a reasonable estimate of the total energy use across the supply chain.
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