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Figure 3 illustrates the proportion of survey responses which successfully progressed through each of the methodology assessment points as described in Table 2.
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The third finding was that each of the methodologies has advantages and disadvantages, and the best method will be function of the available data, the application, and the goals of the modeler.
It was also found that each of the methodologies has advantages and disadvantages, and the best method will be the function of the available data, the application, and the goals of the modeler.
Moreover, we provide a list of the data requirements necessary to implement each of the methodologies.
Each of the methodologies highlighted in this issue involves substantial research and development investment and expertise.
Additional information on each of the methodologies (including descriptions of the R packages used, but excluding the plume metrics, which are added in the latest version of GWSDAT) can be found in Jones et al. (2014).
Secondly, we analyse the strengths and pitfalls of each of the methodologies and provide a data requirement framework containing all the variables and data that need to be taken into account in order to address the problem thoroughly.
At each stage of the methodology critical aspects are highlighted and some solutions are provided.
The overview of each phase of the methodology is important because it sets standards and expectations for the IT leaders.
For the sake of clarity, each stage of the methodology is presented in detail on this particular example.
In particular, we selected different models as possible candidates for each step of the methodology and compared their predictions with measured data to identify the most suitable ones.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com