Séminaire Dyneco de Jacques Populus Dyneco Lebco le Mardi 3 juillet à 11 h dans la salle de réunion DYNECO
After a time when observations of the sea have been made for specific purposes, e.g. for specific national purposes or to demonstrate a technological capability, the European Commission has now moved to a new paradigm where data are collected once and used them for as many purposes as possible. This means relying preferably on users rather than on producers to assess existing data sets and sources and promote recommendations for a better satisfaction of their needs. The EMODNET Atlantic checkpoint (http://www.emodnet-atlantic.eu/) was designed to evaluate the fitness-for-use of current observations and data assembly programs towards 11 marine applications and prioritizing the needs to optimize monitoring systems at the scale of the North Atlantic Ocean.
The methodology documents the fitness-for-use of the existing data by providing indicators of adequacy to the challenge products. The assessment criteria and the development of checkpoint information and indicators are derived from the ISO standards for geographical information and based on standardized catalogues of products and input datasets. To provide a consistent overview of what is needed or available and to reveal the potential synergies among users of the same variable, the results have been classified according to the SeaDataNet vocabulary for marine data.
The issues encountered were classified into the following types:
- Data gaps due to lack of appropriate spatial or temporal resolution or coverage or attributes;
- Assembly needs that could be covered by the creation or update of Thematic Assembly Centres (TACs), which concerns overly scattered data;
- Availability restrictions due to policy, lack of information on data quality and other technical issues (e.g. metadata).
Recommendations are given for most pressing needs, based on the most widely used parameters and their economic or environmental relevance, while bearing in mind that where possible assembling existing data is more cost-effective than going out acquiring new ones.