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The selected CASE STUDIES will provide useful information for the achieving the following objectives:

• Improvements at the design stage
• Improvements at the construction stage
• Improvements at the monitoring stage (including the generation of improved work analysis tools)
• Improvements at the training stage (sector specific training modules generation). By achieving the preceding objectives an increase in the specialization level of the ecoengineering sector within the Mediterranean eco-region will also be accomplished.

THE AIM OF THIS TEMPLATE

• This template offers a possibility to show and share the main information and conclusions of the bioengineering work analysis. This template is readily connected to protocols 2 and 3 aims and contents.
• The contents of the bioengineering work analysis collected throughout the ECOMED project, will allow the generation of improvements at the different levels of analysis. These improvements will both be used in the generation of sector specific tools and improved training modules contents and support the specialisation process of the Mediterranean bioengineering sector.

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THE AIM OF THIS PROTOCOL

This Protocol 2 is about defining the existing project information and the set of variables to be measured in the eco-engineering work analysis and field work. For the generation of the bioengineering work analysis report, please follow the link for more info.

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THE AIM OF THIS PROTOCOL

This field protocol describes the methods that will be employed by the field teams under the ECOMED project. The field protocol aims to encourage the standardisation that is necessary to enable data from the construction site analysis to be pooled for generating improvements at the following levels:
- Design of the bioengineering works
- Constructive procedures of bioengineering works
- Monitoring, maintenance, and long term performance (resilience) of bioengineering works
The descriptions of the methods are intended to be a reference to the best practice for the Mediterranean region and to highlight any pitfalls that might impair the quality of the data.