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
Hot steel rolling is amongst the most important industrial techniques because of huge amount of consumed resources, immense environmental impact, and the significance and enormous quantity of long products. Criteria for improving rolling operations include process efficiency, resource consumption, system reliability, product quality and ergo-ecological sustainability. There is an increasing availability of information within and beyond the domain of forming by rolling. With advances in computerised information processing, it becomes apparent that further progress is to be sought in intelligently combining the strategies and theories developed in differing disciplines. The key to optimising rolling systems is to be found in hybrid models. This approach calls for utilising cross-disciplinary knowledge, including a selection amongst methods such as stochastic, fuzzy and genetic modelling, process control and optimisation as well as supply chain and maintenance management. Evidence obtained by experiments using small-scale chemo-physical modelling encourages the use laboratory rolling for preliminary validations. Research strategy is conceptualised on the basis of a knowledge-based hybrid model. The sample space for this model is constituted by the rolling passes translated into the form of vectors. An example of a rolling pass translation into the vector form is presented.
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