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
Machine learning is a branch of computer science, which oversees the study and construction of algorithms that learn from data. Out of the various machine-learning concepts, this paper talks about 6 clustering algorithms: k-means, DBSCAN, OPTICS, Agglomerative, Divisive and COBWEB. The paper incorporates the performance analysis of these clustering algorithms when applied to FAO Soya bean dataset. The algorithms are compared on the basis of various parameters, such as time taken for completion, number of iterations, and number of clusters formed and the complexity of the algorithms. Finally, based on the analysis, the paper determines the best befitting algorithm for the FAO Soya bean dataset.
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