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
Recent advances in Semantic Web technologies have led to a growing popularity of the Linked Open Data movement. Only recently, the Danish government has joined the movement and published several datasets as Open Data. These raw datasets are difficult to process automatically and combine with other data sources on the Web. Hence, our goal is to convert such data into RDF and make it available to a broader range of users and applications as Linked Open Data. In this paper, we discuss our experiences based on the particularly interesting use case of agricultural data as agriculture is one of the most important industries in Denmark. We describe the process of converting the data and discuss the particular problems that we encountered with respect to the considered datasets. We additionally evaluate our result based on several queries that could not be answered based on existing sources before.
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