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
This paper proposes a new generation of decision_support Systems (DSS) that leverages Web Services and Web 2.0 technologies to allow for new possibilities in the areas of irrigation decision_support. A new classification system for existing DSS, based on their 'network paradigm' is presented with current systems being placed in categories 1 to 5. Category 1 DSS are those with no networking abilities and are typically represented by desktop applications. Category 2 (C2) are those with direct network links to local equipment, such as sensors in a paddock. Category 3 (C3) DSS use local area networks to access data from such sources as databases and networked sensors. C4 DSS use large, proprietary and purpose-built, networks, such as SCADA networks, to collect data as well as using resources available to C3 DSS. C5, use the internet to access multiple instances of the resources available to C4 DSS. We present some examples of DSS in each of these categories for illustration. A further category, 6, is proposed here that uses new internet software technology to extend DSS functionality into uncharted waters. Technologies such as extensible Mark-up Language (XML) and Web Services are proposed to allow DSS to provide different types of support to users at many different levels, to allow for the addition of User Defined Data Sets (UDDS) and to utilise the power of machine-to-machine communications over the internet. We suggest how potential DSS, using some of the technologies mentioned here, may help counter the poor uptake of DSS in Australian agriculture by addressing one of its supposed root causes: that of the lack of user customisation. We propose that in addition to this, a category 6 DSS may be used in a way that no DSS has currently been used and that is in irrigation benchmarking. Further to this: we suggest how a C6 DSS used for irrigation support may present usage metrics for use by 3(rd) parties, such as water supply companies. We then propose back-end architecture for a C6 DSS that utilises technologies such as XML-based Web Services, live, online databases and data fusion to bring together and interpret data from distributed providers. We relate how flexible back-end architecture may allow DSS to provide very customizable decision_support and how sophisticated networking may be used to generate benchmarking data. Next we look at how new approaches to interface design using recent 'Web 2.0' technologies, such as AJAX, provide the tools needed by developers to create DSS front ends that can effectively use the DSS back-ends discussed above.
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