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 advent of Geographical Information Systems (GIS) brought into play an array of tools to interpret the ever growing multitude of geographical variables. The fact that these tools exist does not, nonetheless, mean that it's easy or clear to make valid interpretations of the available information, which ultimately means that the validity of the methods of interpretation may not be enough to make clear planning decisions. The present contribution aims to demonstrate that the geographical nature of available data is in itself an object of interpretation that must precede the use of the GIS tools, and that they must be presented to the user as a flow of reasoning, a logic-based representation of problem-solving steps. In what concerns this presentation, the authors will present what is, from their point of view, one of the most useful methods of visualizing these steps and also one of the most practical tools for local planning. The human interpretation of geographical information is vital, because it is the user that decides the nature of the relations between the data, based on its individual attributes. Also, the manner in which the information is viewed is also important because the choice of used tools is based on an ontological view of the geographical science. Some users prefer to act using only vector data set claiming that it's the best way to represent reality, and other users state that raster data set is the best base to solve a problem providing it has a good enough resolution. The authors argue that the best way to proceed is to identify the nature of the geographical variable and to use vector or raster data considering which is most suitable for each variable. Ultimately the decision between raster or vector data lays upon the decision making agents and what they want as a final output. The article points out that the use of the ArcGis tool Model Builder is the best way to view the flow of operations and that the models built in this way are extremely valuable for local governments (assuming that the same problem may afflict different regions). As such, it is stated that the first step for GIS based problem solving is the clear distinction between context, structure, support and created information and that the models have to reflect these relations. The models also have to solve the vector/raster data problem effectively and they have to be as simple as possible. To demonstrate the stated the authors present one model to point out the preferable methodology and ontological approach of problem solving (the model being about the problem of finding the location of a Wastewater Treatment Plant in the municipality of Amadora, Portugal) and afterwards several examples of Models created with the Model Builder tool that aim to solve specific problems (Risk cartography for Urban Fires in Lisbon, Portugal; Preferable areas for the implementation of a network for urban cycling in Lisbon, Portugal; Preferable areas for the implementation of a network of urban agriculture plots).
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