e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Cage culture effects on mullets (Mugilidae) liver histology and blood chemistry profile


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.

  • HR
    Data keywords
    • machine learning
    Agriculture keywords
    • farm
    Data topic
    • big data
    • modeling
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
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      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.