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
Bacterial, fungal and archaeal microbiota was analysed in 143 chicken faecal samples from a single poultry farm. After DHPLC (denaturing high performance liquid chromatography) 15 bacterial groups, 10 fungal groups and a single archaeal species were differentiated. Samples were grouped into two clusters with significantly different frequencies of C. difficile positive and negative samples in each cluster. Acidaminococcus intestini, described here for the first time as a part of poultry faecal microbiota, was significantly more likely present in C. difficile negative samples, while presence/absence of some other microorganisms (Enterococcus cecorum, Lactobacillus galinarum, Moniliella sp. and Trichosporon asahii) was close to significance. Two other groups not reported previously for poultry, Coprobacillus sp. and Turicibacter sp. did not differ significantly between C. difficile positive and negative samples. Differences in microbiota diversity depend on animal age, but not on the presence of C. difficile. With machine learning (WEKA J48) we have defined specific combinations of microbial groups predictive for C. difficile colonisation. Microbial groups associated with C. difficile colonisation in poultry are different than those reported for humans and include bacteria as well as fungi. Also with this approach A. intestini was found to be most strongly related to C. difficile negative samples. (c) 2013 Elsevier B.V. All rights reserved.
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