Abstract

Bio-phenomena interpretation case studies are based on the application of scientific models. Usually, these studies are based on previous experiences. This paper shows how to provide for a better support to bio-phenomena scientists and specialists, on exchanging scientific programs, models and data. We specifically describe the Cabiunas case study.

Introduction

Corrosion monitoring on oil platforms over the Brazilian coastal zone is one of the main concerns of scientists from CENPES-Petrobrás. Some of these scientists are chemists, biologists and engineers that study corrosion caused by bacteria. To identify the main cause of bio-corrosion events such as oil spills, teams of specialists have to collect heterogeneous distributed data and apply an adequate scientific model. For example, they collect water or pipe samples from the region under investigation. Then, laboratory analyses provide numerical data sets from these samples, which are then interpreted or analyzed by means of scientific models and programs in order to derive new data, or some useful conclusion.

The analysis of oil spills usually requires combining multiple models originating from different disciplines. The choice of a model is usually guided by the experience of the scientist previous case studies. Once models are chosen, scientists can run the corresponding programs. However, choosing the right models and running the adequate programs are done empirically. In addition, previous successful case studies from other scientists are hard to reuse. Model characteristics are described in several ways, and the experience from a successful model application is not always registered in paper reports. Consequently, scientists have difficulties in model management activities, such as, comparing different models, finding associations between models and programs, and more importantly, taking advantage from a large number of previous experiences. Moreover, in a multidisciplinary and distributed scientific environment, scientists and specialists need to understand models out of their scope of expertise and use remote data and programs. Thus, it is important to describe and represent scientific models as well as their associated program implementations. They are considered important scientific resources that need to be organized and shared.

This paper presents how the Scientific Resource Management architecture can help corrosion specialist teams on bio-phenomena interpretation case studies. The next section explains this architecture. The third section presents the metamodel used to describe scientific resources. The fourth section describes the Cabiunas case study. Finally, the last section concludes the paper.

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