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
The online Data Acquisition system of the Compact Muon Solenoid (CMS) experiment at CERN's Large Hadron Collider is designed to collect data corresponding to a single collision of particles, referred to as an event, from about 500 detector Front-Ends. Each of those Front-Ends delivers event-fragments of an average size of 2KB at a rate of 100 kHz. The event-fragments are statically distributed (usually in round robin fashion) between 8 identical computing farms, which construct the whole events. In this paper we present experimental results of employing a distributed, asynchronous load scheduling algorithm in place of the static event allocation mechanism. The research focuses in particular on balancing the event flow in case of degradations in computing power or network throughput. The discussed studies prove that the proposed method meets the requirements of CMS experiment and has a positive impact on the resource utilization and overall fault tolerance.
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