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
Toward Collaborative LCA Ontology Development: a Scenario-Based Recommender System for Environmental Data Qualification
This paper describes a collaborative approach to ontology development for data qualification for life cycle assessment by taking into consideration the Life Cycle Inventory (LCI) and Data Quality Indicator (DQI). The developed ontology is integrated with rule-based knowledge, to provide user-defined policies for LCI based on DQI. An ontology application management framework is developed to provide a collaborative environment for knowledge engineers and domain experts to define the knowledge explication and recommendation rules based on usage scenario. LCI data from agricultural domain is collected, and mapped to the knowledge base. To demonstrate the advantage of transformed rules, a scenario-based recommender system is built on top of the ontology, and carries out data quality measurement.
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