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


Critical Assessment Indicators for Measuring Benefits of Rural Infrastructure Investment in China


Rural infrastructure is of vital importance for agricultural growth, economic development, and poverty alleviation, particularly in developing countries such as China. In line with the implementation of a Coordinated Urban-Rural Development Strategy, infrastructure investment in China has consciously been tilted to rural areas. An urgent need exists to assess whether the investment has induced the benefits as expected. Existing research on rural infrastructure investment assessment focuses primarily on economic return while neglecting its social and ecological benefits. This paper identifies a set of critical assessment indicators (CAIs) that can be used to evaluate the multifaceted benefits of rural infrastructure investment in China. Research data were collected through a questionnaire survey given to three groups of experts, including government officers, professionals, and business practitioners who are working in China's housing and urban-rural development sector. Monte Carlo simulation (MCS) is used to generate additional data to supplement the data set from the questionnaire survey. The fuzzy set theory, which appreciates the fuzziness of data from the questionnaire survey, is used in the selection of CAIs. The CAIs can help the local governments in China to make better decisions in investing in rural infrastructure. These critical indicators can also be generalized to provide valuable references for the investigations of rural infrastructure investment in other developing countries. DOI: 10.1061/(ASCE)IS.1943-555X.0000066. (C) 2011 American Society of Civil Engineers.

  • CN
  • Hong_Kong_Polytech_Univ (CN)
  • Univ_Hong_Kong (CN)
Data keywords
  • research data
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • modeling
Document type

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

Institutions 10 co-publis
    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.