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
It is commonly accepted that modelling frameworks offer a powerful tool for modellers, researchers and decision makers, since they allow the management, re-use and integration of models from various disciplines and at different spatial and temporal scales. However, the actual re-usability of models depends on a number of factors such as the accessibility of the source code, the compatibility of different binary platforms, and often it is left to the modellers' own discipline and responsibility to structure a complex model in such a way that it is decomposed in smaller 're-usable' sub-components. What reusable and interchangeable means is also somewhat vague; although several approaches to build modelling frameworks have been developed, little attention has been dedicated to the intrinsic re-usability of components. In this paper we focus on how models can be linked together to build complex integrated models. We review and investigate the various approaches to model linking adopted by a number of Integrated Modelling Frameworks and we aim at describing the advantages and disadvantages of each approach. We stress that even if a model component interface is clear and reusable in software terms, this is not a sufficient condition for reusing a component across different Integrated Modelling Frameworks. This remark reveals the need for adding rich semantics in model interfaces; we do such an attempt through the use of domain classes and ontologies. A domain class can be considered as an abstract data structure for defining a set of a model variables and their attributes (Rizzoli et al. 1998). A model interface (in terms of inputs, outputs, states and parameters) can be defined using a domain class, providing some advantages: first of all, an instance of a domain class can be accessed at run-time to supply the model component with the appropriate data. Secondly, it annotates model variables with attributes that can be used for pre-post condition checks. Thirdly, it supports compliance with the requirement that asks for model components to be separated from their data structures. And, last but not least, it provides an easy way for linking model components at a higher level. This practice uses shared domain classes for interchanging data across models, taking full advantage of component-based software engineering primitives. Then, we present an approach based on the formalisation of ontologies to describe models' interfaces and relationships. The use of ontologies is advantageous as it (a) supports the automatic generation of code templates for models and domain classes in different Integrated Modelling Frameworks, (b) it facilitates the application of a reasoner (inference engine) on the structured knowledge, which can detect abnormalities or conflicts in model interfaces, and (c) it supports model linking in a contentenriched way, which can be proven valuable for avoiding common problems related to poor semantics of model interfaces. Finally, this paper presents a working example of an ontology formalisation developed for the Seamless project(1). This ontology (called SeamAg) aims to formally describe biophysical models related to agronomic and environmental domain to be developed by a large community of modellers within the Seamless project. Modellers' knowledge, related to model subsystems, variables and interfaces, is kept separated from the actual implementation. The use of the SeamAg ontology for storing model interfaces supports the independence of software design choices from modelling knowledge, which be easily reused, integrated in different environments, or shared with third parties. The potentials of extending the presented ontology-driven approach is discussed not only for model linking, but also in the context of building model component workflows using web services.
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