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
An improved blended data method was developed for preparation and generation of solar radiation gridded datasets for SILO; Queensland Government database containing point and gridded daily climate data for Australia from 1890 till present designed for crop and pasture modelling. The new blended data method incorporates three sources of solar radiation data: radiometer measurements, sunshine duration, and cloud-cover observations. The new method converts all data sources to the percentage extra-terrestrial radiation using new conversion equations derived from the experimental data and thus the conversion tables previously used are now redundant. Comparison with satellite derived estimates shows that the blended data method has reduced bias compared to the previous method. The blended data method addresses the need for historical pre-satellite solar radiation gridded datasets for climate and agricultural modelling, model calibration, and computation of synthetic evaporation rates. (C) 2013 Elsevier Ltd. All rights reserved.
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