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
Nowadays, there has been an increment of open data government initiatives promoting the idea that particular data should be freely published. However, the great majority of these resources is published Man unstructured format and is typically accessed only by closed communities. Starting from these considerations, in a previous work related to a dataset on young workers on non permanent contracts, we proposed an experimental and preliminary methodology for facilitating resource providers in publishing public data into the Linked Open Data (LOD) cloud, and for helping consumers (companies and citizens) in efficiently accessing and querying them. Linked Open Data play a central role for accessing and analyzing the rapidly growing pool of life science data and, as discussed in recent meetings, it is important for data source providers themselves making their resources available as Linked Open Data. In this paper we extend and apply our methodology to the agricultural domain, i.e. to the CEREALAB database, created to store both genotypic and phenotypic data and specifically designed for plant breeding, in order to provide its publication into the LOD cloud. (C) 2014 Elsevier B.V. All rights reserved.
Inappropriate format for Document type, expected simple value but got array, please use list format