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
A comparative study of blood chemistry and histology was conducted on two groups of mullets (Mugilidae) living under different conditions with different feed sources. The aquaculture influenced mullet group (AIM), was collected near fish farms and the control group of mullet (CM) was caught in the waters Without any aquaculture activities. Histological and biochemical procedures were employed to Study liver histomorphology, plasma aspartate and alanine aminotransferase (AST, ALT), triglyceride (TRIG), cholesterol (CHOL), glucose (GLU) and total protein (TP) of both AIM and CM. Moderate histological changes (lipid infiltration) were observed in the liver of AIM. Significant changes in plasma variables were observed in AIM. Blood chemistry variables measured proved to be good indicators of artificial feed effects. Classical statistical approaches were applied to the blood chemistry and histopathology data. For the first time machine learning techniques were used to generate comprehensible classification models and to explore blood chemistry variable importance, strength, their mutual interactions or dependencies, and to investigate reliability of particular variables within the groups. (C) 2008 The Authors. Journal compilation (C) 2008 The Fisheries Society of the British Isles.
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