MacroBen

Often marine biological data are the result of projects with a limited temporal and spatial cover. By combining the small datasets obtain by such individual projects, massive databases can be created that make large scale analyses possible. Such datasets are especially important when one considers the recent observation of major shifts of marine species due to global change. Within the [www.marbef.org MarBEF] working group Global Patterns of Marine Biodiversity across Ecosystems (Theme 1), an integrated database (MacroBen) on soft-bottom macrobenthos was compiled.

Constituent datasets

Each dataset was archived and described in detail at the data centre of the Flanders Marine Institute (VLIZ). Describing these datasets in a standardised way made it possible to create a searchable metadata inventory. This helps scientists to discover desired data and also enables them to share their data with other scientist. All metadata descriptions are publicly available. Archiving the datasets prevents them from being lost by ensuring the long-term integrity of the data. Most of the child databases (29 of the 44) can be found here.

Integrating the datasets

The MacroBen database integrates datasets on softbottom macrobenthic fauna from all European seas. A total of 44 datasets was collected, harmonised and integrated into a relational Access database. The integrated database includes both quantitative and qualitative data on organisms and several abiotic parameters ranging from the northeast Atlantic and the North Sea, Norwegian Sea and Arctic Ocean, the Baltic Sea and the Mediterranean and Black seas. During harmonisation of the different datasets problems occurred because not all datasets used the same taxonomy, didn't mention which datum system they used or used different sampling methods.

The data management of MacroBen is based on a series of Access databases: 1 Access database for each constituent dataset, and 1 to contain the integrated data. The latter also contains tools and a simple user interface to work with the data. This system made it possible to manage the data from individual datasets separately, and go through an iterative process of data cleaning and harmonisation.