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
This paper presents a method for clustering food offers based on the cuckoo search algorithm. The proposed method clusters food offers based on the similarity between their nutritional features (e.g. calcium, vitamins etc.) and/or ingredients. The similarity is evaluated by using the Sorensen Dice coefficient. To test the clustering method proposed here, we have developed in-house a set of 800 food offers. The food offers have been generated as starting from a set of food recipes (provided in an XML standard for sharing recipes) and a database containing information about nutritional features. This database stores the nutritional features of each food type, as provided by the Agricultural Research Service of the United States Department of Agriculture. We evaluated the performance of our clustering method by using the following metrics: the Dunn Index, the Davies-Bouldin index, and the Average Item-Cluster Similarity.
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