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.

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A knowledge-based approach for classifying lake water chemistry


Knowledge-based systems are computer models that facilitate reasoning such that human experience and expertise can be represented and made available to non-specialists. In this paper we describe the application of a knowledge-engineering methodology, using the NetWeaver (TM) software, to the problem of lakewater acid-base chemistry assessment. We present, and document with examples, the structure, arguments, and criteria values of a knowledge-based decision_support system for classifying lakes in five acid-sensitive regions of the United States. We also discuss the significance of this software tool for federal land managers in the management of aquatic resources in national parks, national wildlife refuges, and wilderness areas to protect against water quality degradation associated with atmospheric deposition of sulfur and nitrogen. The Lake Chemistry knowledge bases have undergone repeated testing by members of a lake chemistry domain expert panel. There is agreement among the panel that these regional models provide accurate classifications of lakewater chemistries. The graphical and executable rendering of knowledge bases within NetWeaver (TM) greatly facilitates the knowledge engineering process, as it permits the inclusion of the domain expert(s) in the knowledge representation process and hence encourages greater participation in the design of the final knowledge-based model. In addition, the inclusion of fuzzy arguments, against which data values can be compared, greatly reduces the potential for combinatorial explosion that often occurs in expert systems that rely on categorical data interpretation, while at the same time providing a robust description of complex systems. It is our expectation that adoption of this approach, and others like it, will stimulate further development of knowledge-based systems for agriculture, natural resource management, and other complex decision_support arenas. (c) 2004 Elsevier B.V. All rights reserved.

  • US
  • Penn_State_University_Pk (US)
  • US_Natl_Pk_Serv (US)
  • Univ_Montana_Missoula (US)
Data keywords
  • knowledge
  • knowledge based
  • knowledge engineering
  • reasoning
  • knowledge representation
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • semantics
Document type

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

Institutions 10 co-publis
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    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.