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
Using a POWDER Triple Store for boosting the real-time performance of global agricultural data infrastructures
As the trend to open up data and provide them freely on the Internet intensifies, the opportunities to create added value by combining and cross-indexing heterogeneous data at a large scale increase. To seize these opportunities we need infrastructure that is not only efficient, real-time responsive and scalable but is also flexible and robust enough to welcome data in any schema and form and to transparently relegate and translate queries from a unifying end-point to the multitude of data services that make up the open data cloud. Transparent relegation and translation relies on detailed and accurate data summaries and other data source annotations, and with increased data volumes and heterogeneity managing these annotations, it becomes by itself a challenging data problem. In this position paper we discuss (a) how a scalable and robust semantic storage can be developed, using indexing algorithms that can take advantage of resource naming conventions and other natural groupings of URIs to compress data source annotations about extremely large datasets; and (b) how query decomposition, source selection, and distributed querying methods can be designed, that take advantage of such algorithms to implement a scalable and robust infrastructure for data service federation.
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