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
We report an in-vitro autofluorescence spectroscopic study of cow eye tissue to explore the applicability of the approach in discriminating early stage "cancer eye" from normal squamous eye tissues. Significant differences were observed in the autofluorescence signatures between the "cancer eye" and normal eye tissues. The spectral differences were quantified by employing a probability-based diagnostic algorithm developed based on recently formulated theory of Relevance Vector Machine (RVM), a Bayesian machine-learning framework of statistical pattern recognition. The algorithm provided sensitivity and specificity values of 97 +/- 2 % towards cancer for the training set data based on leave-one-out cross validation and a sensitivity of 97 +/- 2 % and a specificity of 99 +/- 1 % towards cancer for the independent validation set data. These results suggest that autofluorescence spectroscopy might prove to be a quantitative in-vivo diagnostic modality for early and accurate diagnosis of "cancer eye" in veterinary clinical setting, which would help improve ranch management from both economic and animal care standpoint.
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