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 real time data analysis at next generation experiments is a challenge because of their enormous data rate and size. The SuperKEKB experiment, the upgraded Belle experiment, requires to process 100 times larger data of current one. The offline-level data analysis is necessary in the HLT farm for the efficient data reduction. The real time processing of huge data is also the key at the planned dark energy survey using the Subaru telescope. The main camera for the survey called Hyper Suprime-Cam consists of 100 CCDs with 8 mega pixels each, and the total data size is expected to become comparable with that of SuperKEKB. The online tuning of measurement parameters is being planned by the real time processing, which was done empirically in the past. We started a joint development of the real time framework to be shared both by SuperKEKB and Hyper Suprime-Cam. The parallel processing technique is widely adopted in the framework design to utilize a huge number of network-connected PCs with multi-core CPUs. The parallel processing is performed not only in the trivial event-byevent manner, but also in the pipeline of the software modules which are dynamically placed over the distributed computing nodes. The object data flow in the framework is realized by the object serializing technique with the object persistency. On-the-fly collection of histograms and N-tuples is supported for the run-time monitoring. The detailed design and the development status of the framework is presented.
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