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
CMS has a distributed computing model, based on a hierarchy of tiered regional computing centers and adopts a data driven model for the end user analysis. This model foresees that jobs are submitted to the analysis resources where data are hosted. The increasing complexity of the whole computing infrastructure makes the simple analysis work flow more and more complicated for the end user. CMS has developed and deployed a dedicated tool named CRAB (CMS Remote Analysis Builder) in order to guarantee the physicists an efficient access to the distributed data whilst hiding the underlying complexity. This tool is used by CMS to enable the running of physics analysis jobs in a transparent manner over data distributed across sites. It factorizes out the interaction with the underlying batch farms, grid infrastructure and CMS data management tools, allowing the user to deal only with a simple and intuitive interface. We present the CRAB architecture, as well as the current status and lessons learnt in deploying this tool for use by the CMS collaboration. We also present the future development of the CRAB system.
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