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
On Analyzing the Degree of Coldness in Iowa, a North Central Region, United States: An XML Exploitation in Spatial Databases
State of Iowa is an agricultural rich state in north central region and is divided into 99 counties. NCRA in the United States maintains agricultural databases to facilitate crop and risk analysis, pest management and forecasting. NC94 is one such dataset which is intensively used and is available for public use through many sources to process and analyze to get future predictions about agriculture. In this work we calculate the cumulative degree of coldness in Iowa with spatial granularity as county in last 30 years. To demonstrate the degree of coldness, we choose blue as the base color and counties are rendered with different shades of blue color based on the degree of coldness. Higher intensity of the color reflects the higher coldness whereas the lower intensity corresponds to lower coldness. We expect that the results of this research provide direct benefits to farmers and will attract the attention of agricultural/ computational scientific community.
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