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
Performance Evaluation of South Esk Hydrological Sensor Web: Unsupervised Machine Learning and Semantic Linked Data Approach
Technological progress has lead the sensor network domain to an era where environmental and agricultural domain applications are completely dependent on hydrological sensor networks. Data from the sensor networks are being used for knowledge management and critical decision_support system. The quality of data can, however, vary widely. Existing automated quality assurance approach based on simple threshold rulebase could potentially miss serious errors requiring robust and complex domain knowledge to identify. This paper proposes a linked data concept, unsupervised pattern recognition, and semantic ontologies based dynamic framework to assess the reliability of hydrological sensor network and evaluate the performance of the sensor network. Newly designed framework is used successfully to evaluate the South Esk hydrological sensor web in Tasmania, indicating that domain ontology based linked data approach could be a very useful methodology for quality assurance of the complex data.
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