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
Agriculture Data for All - Integrated Tools for Agriculture Data Integration, Analytics and Sharing
Agriculture produces an abundance of data in both public and private domains. Such data include, but are not limited to: national soil databases, long-term data on carbon balance across different climate zones and vegetative land covers, digital elevation models, regional and national inventories, remote sensing data, geophysical data, socio-economic and many other data sets. These agriculture-related records are interesting not only to the agriculture sector. Ecology, environment, business, policy, various sciences, etc. can use this data for their discoveries. They can investigate the impact of land-management approaches, such as fertilization, grazing, irrigation, and more. Agriculture data analysis can help understand the problems and lead policy makers to implementing risk mitigation and restoration strategies.
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