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
During the last years, considerable progresses have been made in developing on-line species occurrence databases. These are crucial in environmental and agricultural challenges, e.g., they are a basic element in the generation of species distribution models. Unfortunately, their exploitation is still difficult and time consuming for many scientists. No database currently exists that can claim to host, and make available in a seamless way, all the species occurrence data needed by the ecology scientific community. Occurrence data,are scattered among several databases and information systems. It is not easy to retrieve records from them, because of differences in the adopted protocols, formats and granularity. Once collected, datasets have to be selected, homogenised and pre-processed before being ready-to-use in scientific analysis and modelling. This paper introduces a set of facilities offered by the D4Science Data Infrastructure to support these phases of the scientific process. It also exemplifies how they contribute to reduce the time spent in data quality assessment and curation thus improving the overall performance of the scientific investigation. (C) 2014 Elsevier B.V. All rights reserved.
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