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
Research questions at the regional, national and global scales frequently require the upscaling of existing models. At large scales, simple model aggregation may have a prohibitive computational cost and lead to over-detailed problem representation. Methods that guide model simplification and revision have the potential to support the choice of the appropriate level of detail or heterogeneity within upscaled models. Efficient upscaling will retain only the heterogeneity that contributes to accurate aggregated results. This approach to model revision is challenging, because automatic generation of alternative models is difficult and the set of possible revised models is very large. In the case where simplification alone is considered, there are at least 2(n) - 1 possible simplified models where n is the number of model variables. Even with the availability of High Performance Computing, it is not possible to evaluate every possible simplified model if the number of model variables is greater than roughly 35. To address these issues, we propose a method that extends an existing procedure for simplifying and aggregating mechanistic models based on replacing model variables with constants. The method generates simplified models by selectively aggregating existing model variables, retaining existing model structure while reducing the size of the set of possible models and ordering them into a search tree. The tree is then searched selectively. We illustrate the method using a catchment scale optimization model with c. 50,000 variables (Farm-adapt) in the context of adaptation to climatic change. The method was successful in identifying redundant model variables and an adequate model 10% smaller than the original model. We discuss how the procedure can be extended to other large models and compare the method to those proposed by others. We conclude by urging model developers to regard their models as a starting point and to consider the need for alternative models during model development. (C) 2009 Elsevier B.V. All rights reserved.
Inappropriate format for Document type, expected simple value but got array, please use list format