In many areas of the biomedical sciences you, as a researcher have the task to discover relevant parameters in sometimes large data sets or to match experimental data with a theoretical model. It appears not easy to make a choice from a wealth of specialised, user-friendly, commercial software and to remain critical with respect to the method and the outcome, when using these packages. In order to prevent scientific errors and to improve reuseability of data and software, data-analysis is often carried out using e.g. MATLAB, R and Image-J. Although the latter two are more specialized for statistics and image analysis respectively, these all-purpose programs are available for several computer platforms and might be considered as standard programs for tackling all kinds of data-acquisition, analysis, and visualisation of scientific data in many fields of science. By virtue of their open character, you are free to adapt its functionality and to design new functionality to tailor the program to your own needs.

In this practical course, you will develop experience in the use of a selection of the most important scientific software suites. Training in the use of these programs may prove advantageous in later stages of your study or career when research data have to be analysed or when literature data have to be critically assessed. Moreover, armed with this experience, you are able to understand and to adapt programs or scripts in other programming languages when needed. Emphasis is placed on MATLAB as an all-round, full-fledged programming environment. Elements of the syntax and the use of function M-files are treated and applied to research questions from the biomedical sciences (data analysis, statistics and graphical presentation) which you will elaborate with a fellow-student. Case studies are selected from  biochemistry, bioinformatics, cell biology, pharmacology, neurosciences, physiology, hematology and cardio-vascular studies.  You are invited to work on research projects of your own choice.