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
It is a significant task to extract market data from different Web pages for prediction and analysis. A prototype decision_support system of an agricultural product market is designed and developed in this paper. It can extract online price information of a certain agricultural product from websites of agricultural wholesalse, predict the product price in the future months, and provide further decision_support on such issues as which cities the product should be sent to for sale and which cities should be in the transport route. To achieve these goals, an algorithm named MDT-E (Market Data Table Eextraction) is proposed to extract the maximum data table in a Web page. Based on the common practice that "the price data are usually displayed in the largest table on a Web page with the structure of "< td >" and "</td >" tags", our market data extraction algorithm detects the largest table on a Web page at first, then transforms the table into a DOM tree,and further obtains the node values of the "< td >" tags. This algorithm can automatically detect market data without an assigned data extraction region. The designed system uses a quadratic forcasting model of linear time series to predict the price, and compares the prediction results by using different time series and different sample data to find the best forecasting model to forecast the price in cites. In addition, it provides the decision_support to determine the transport route based on the transport costs and product prices.
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