ON CORRELATING BIRD MIGRATION TRAJECTORY WITH CLIMATE CHANGES

Climate changes are expected to affect bird migration in several aspects including timing changes, breeding and migration orientation. The correlation analysis of several climate conditions (e.g. temperature, wind, humidity, etc) and bird migration trajectory is the key for explaining bird behavior during migration. Moreover, the resulting correlation can be used for predicting new bird behavior according to climate changes. In this paper we propose an integrated solution for correlating bird migration trajectory with climate conditions. This solution is composed by two orthogonal and complementary methods. The first method concerns discovering regions where birds are used to stop during their migration. The second method is based on a machine learning algorithm for classifying bird stops according to climate conditions. A real bird migration scenario was used for assessing the accuracy of the integrated solution

Towards a Scientific Model Management System

Computational models of biological systems aim at accurately simulating in vivo phenomena. They have become a very powerful tool enabling scientists to study complex behavior. A side effect of their success Unfortunately exists and is observed as an increasing difficulty in managing data, metadata and a myriad of programs and tools used and produced during a research task. In this work we aim at supporting scientists during a research endeavour by using Scientific Models as a main guiding element for describing, searching and running computational models, as well as managing the corresponding results. We assume a data-oriented perspective for scientific model representation materialized into a data model with which users describe scientific models and corresponding computational models, and a query language with which a scientist specifies simulation queries. The model is grounded in XML and tightly related to domain ontologies, which provide formal domain descriptions and uniform terminology. Scientists may search for scientific models and run simulations that automatically invoke the underlying programs on provided inputs. The results of a simulation may generate complex data that can be queried in the context of the scientific model. Higher-level models can be specified through views that export a unified representation of underlying scientific models