While the data analysis and sampling design methods that can be applied depend strongly on the situation and specific goals of initial nuclear site characterization, the overall strategy often takes the form of the generic workflow illustrated in this flowchart.

The starting point we consider here is the request for initial nuclear site characterization to a radiological characterization team. Such a request can come from different kinds of actors, and can come with different amounts of detail. Following this request, a clear list of all objectives and identification of the constraints is absolutely required, and might ask for some iterations with the requester to agree on the goals and priorities. The highest-priority objective should in most cases be tackled first, and the cycle along the different objectives is started.

All prior information that is available and relevant for the investigated case should be gathered as a first step. If some data would already be available, a first analysis to check if the objective is achieved is probably very useful, even if the results come with lots of uncertainty. Such an analysis consists, in general, of the following steps: Pre-processing, exploratory data analysis, the actual data analysis, and potentially a post-processing step. If the objective is not achieved, a sampling design should be proposed using the most appropriate method(s) given all prior information and the data analysis result, and the corresponding characterization campaign should be performed. Additional characterization can reveal unexpected issues, and often revisiting the gathering of prior information is then useful. After the additional characterization, the data is again analyzed, and the iterative procedure is continued until the objective is finally reached, after which the remaining objectives can be tackled. Once all objectives have been achieved, the initial characterization study should be reported in a transparent way, making clear what has been measured, which results were obtained from data analysis, and how large the corresponding uncertainty is. The different steps are more extensively discussed one by one below.