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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Cycle Time Prediction in Wafer Fabrication Line by Applying Data Mining Methods


Wafer fabrication is considered the most complex and costly challenge in the semiconductors industry. Cycle Time (CT), which denotes flow time, is one of its key performance measures. This work develops CT prediction models by applying Machine Learning (ML) and Data Mining (DM) methods. The models can assist in improving manufacturing and supply chain efficiency. They rely on historical production line data taken from the fab's Manufacturing Execution System (MES), and include wafer lot processing details of various operations. The prediction is done for an average CT of a single lot, processed through a single operation step. Two types of classification techniques are used. The best fitted Decision Trees (DT) model achieves 76.5% accuracy, and the best Neural Network (NN) model (two hidden layers) achieves 87.6% accuracy. The significance of this study is in establishing dynamic CT prediction models, which can be used to predict CT of a single operation step, a line segment or a complete production line.

  • IL
    Data keywords
    • machine learning
    Agriculture keywords
    • supply chain
    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.