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
The software platform SIEVE can be used to automatically build immersive and interactive 3D landscape environments from spatial data infrastructures. These environments can be viewed with SIEVE Viewer, the visualisation component, which also provides a virtual collaboration platform. We are currently developing an application platform within SIEVE that will interface with the Victorian eResearch Strategic Initiative Ecoinformatics project on climate change. SIEVE is used as a 3D visualisation front-end for a number of different climate change models. Landscape scenarios will show the visual impact and the effect on agricultural productivity due to climate change, dependent on temperature rise, precipitation change and solar radiation. SIEVE can show temporal changes through the decades between 2000 and 2050. Another feature of SIEVE is that it can demonstrate the predicted sea level rise in coastal areas. SIEVE has the capability to show realistic representations of existing vegetation based on local vegetation species and geotypical manmade objects (buildings, sheds, etc). This enables communities to view their landscapes and how they change over the decades to 2050. We also implemented an abstract/scientific visualisation path. This allows the end user to view climate variable maps as the ground texture and show stylised icons on the surface, for example to show change in agricultural productivity. Landscapes models in SIEVE are integrated into a collaborative platform. End users can share environments through the Internet and inspect and discuss the virtual landscapes and ultimately make decisions to adapt to predicted future climate change scenarios. The climate data has been generated by models from Monash University, Victoria, Australia and productivity maps are generated by the Department of Primary Industries, Victoria, Australia. This paper's emphasis is on the presentation of the climate change maps in a collaborative virtual environment in SIEVE.
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