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
Evaluating the Practical Applicability of Thesaurus-Based Keyphrase Extraction in the Agricultural Domain: Insights from the VOA3R Project
The use of Knowledge Organization Systems (KOSs) in aggregated metadata collections facilitates the implementation of search mechanisms operating on the same term or kyphrase space, thus preparing the ground for improved browsing, more accurate retrieval and better user profiling. Automatic thesaurus-based keyphrase extraction appears to be an inexpensive tool to obtain this information, but the studies on its effectiveness are scattered and do not consider the practical applicability of these techniques compared to the quality obtained by involving human experts. This paper presents an evaluation of keyphrase extraction using the KEA software and the AGROVOC vocabulary on a sample of a large collection of metadata in the field of agriculture from the AGRIS database. This effort includes a double evaluation, the classical automatic evaluation based on precision and recall measures, plus a blind evaluation aimed to contrast the quality of the keyphrases extracted against expert-provided samples and against the keyphrases originally recorded in the metadata. Results show not only that KEA outperforms humans in matching the, original keyphrases, but also that the quality of the keyphrases extracted was similar to those provided by humans.
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