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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Wineinformatics: Applying Data Mining on Wine Sensory Reviews Processed by the Computational Wine Wheel


As the world becomes more digital, data Science is the successful study that incorporates varying techniques and theories from distinct fields. Among all fields, the domain knowledge might be the most important since all data science researchers need to start with the domain problem, and end with useful information within the domain. Identifying new application domain is always considered as fundamental research in the area. Wine was considered as a luxury in old days; however, it is popular and enjoyed by a wide variety of people today. Professional wine reviews provide insights on tens of thousands wines available each year. However, currently, there is no systematic way to utilize those large number reviews to benefit wine makers, distributers and consumers. This project proposes a brand new data science area named Wineinformatics. In order to automatically retrieve wines' flavors and characteristics from reviews, which are stored in the human language format, we propose a novel "Computational Wine Wheel" to extract key words. Two different public-available datasets are produced based on our new method in this paper. Hierarchical clustering algorithm is applied on the first dataset and retrieved meaningful clustering results. Association rules algorithm is performed on the second dataset to predict whether a wine is scored above 90 point or not based on the wine savory reviews. 5-fold cross validation experiments are executed based on different parameters and results with a range of 73%similar to 82% accuracy are generated. This new domain will bring huge benefits to fields as diverse as computer science, statistics, business and agriculture.

  • US
  • Univ_Cent_Arkansas (US)
Data keywords
  • knowledge
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
2014 IEEE International Conference on Data Mining Workshop (ICDMW)
Document type

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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.