e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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:

Discover all records
Home page


BSP-GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection


The significance of Internet-of-Things to Supply Chain Management has been dramatically increasing. The performance of supply chain based on Internet-of-Things is largely dependent on its optimization. Genetic algorithms (GAs) are important intelligent methods for complex system optimization problems, but they have some internal drawbacks such as premature and slow convergence to the global optimum. In this paper, we present a new schema protection based GA (BSP-GA). First, we propose three principles for selecting excellent schema based on the schema theory; second, we propose the concept of K-intensive effect synthesis operator, and we give a general five-intensive effect synthesis operator and its proof; third, we give the selection process of excellent schema through an example, and further we give the implementation steps of BSP-GA. The performance of BSP-GA has been compared with simple GA by using two carefully chosen benchmark problems. It has been observed that BSP-GA can yield the global optimum more efficiently than commonly used simple GA. Furthermore, a theorem is presented to guarantee the convergence of BSP-GA. Copyright (C) 2014 John Wiley & Sons, Ltd.

  • CN
  • PL
  • US
  • Hebei_Univ_Sci_&_Technol (CN)
  • Old_Dominion_Univ (US)
  • Tsinghua_Univ (CN)
  • Univ_Warmia_&_Mazury (PL)
Data keywords
  • internet of things
Agriculture keywords
  • supply chain
Data topic
  • big data
  • information systems
  • modeling
  • sensors
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • Old_Dominion_Univ (US)
Powered by Lodex 8.20.3
logo commission europeenne
e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.