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
Data Farming is a process that has been developed to support decision-makers by answering questions that are not currently addressed. It uses an inter-disciplinary approach that includes modeling and simulation, high performance computing and statistical analysis to examine questions of interest with large number of alternatives. Data Farming allows for the examination of uncertain events with numerous possible outcomes and provides the capability of executing enough experiments so that both overall and unexpected results may be captured and examined for insights. In 2010, the NATO Science and Technology Organization started the three-year Task Group "Data Farming in Support of NATO" to assess and document the data farming methodology to be used for decision_support. Two case studies were performed as proof-of-concept explorations to demonstrate the power of Data Farming. The paper describes the Data Farming methodology as an iterative process and summarizes the results of the case studies.
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