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
With the cloud paradigm and the concept of everything as a service (XasS), our ability to leverage the potential of distributed computing resources seems greater than ever. On the other hand, data farming is a methodology based on the idea that by repeatedly running a simulation model on a vast parameter space, enough output data can be gathered to provide an meaningful insight into relations between the model's properties and its behaviours, with respect to the simulation's input parameters. In this paper, we present an extension of a data farming computing platform, named Scalarm, and it's evaluation in the context of molecular dynamics (MD) simulations on heterogeneous resources, such as clusters and cloud systems. As a case study, this paper demonstrates how MD simulations can be run with Scalarm on different infrastructures easily without requiring any modifications to the source code of the original MD simulation program. Finally, results from nano droplet simulation runs are presented, that show the advantages of the Scalarm platform for running MD simulations on a heterogeneous infrastructure - not only for collecting pure numeric data, but also for automated post processing and visualization of the results.
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