Research Cluster “Computing in Science”
Universität Hamburg, with its strengths in both the natural sciences and computer science, offers an excellent environment for interdisciplinary research and teaching. Information technology has advanced into almost all areas of social life today, and has a very long tradition in the natural sciences. Not only technologically sophisticated experiments are dependent on information technologies, scientific data management and worldwide communication are also central cornerstones in the natural sciences. However, the focus is on modelling scientific phenomena. Computer simulations make an important contribution to a better understanding of nature.
In the modern life sciences, computer science is omnipresent under the keyword bioinformatics. A few years ago, the computer started a revolution in genome research. Thanks to state-of-the-art laboratory technology and computer science methods, the human genome with a length of 3.1 billion base pairs has been decoded for the first time. Today, the genomes of many mammals, plants, bacteria and viruses are available for research. Through the further development of the technology, individual genetic information can be used for a better understanding of the processes in living organisms. This is of particular benefit to modern medical research.
Research focus "Data Science"
The Department of Informatics is currently working on the establishment of an interdisciplinary research focus Data Science, which takes up the various preliminary work in the facultative potential area Computing in Science and is to build on further disciplines. New methods for the information processing of big data are to be developed here, for which conventional methods do not work, because the data to be considered are too large, too complex, too fast moving and too weakly structured…
Today, very large amounts of data accumulate in all natural sciences. They are generated in tomographs, sequencers, microscopes and other technical devices, but also as result data in numerical simulations. Especially in the field of climate research, these data are stored for decades. They fill extensive disk storage and tape archives that need to be managed efficiently. Computer science concepts enable global access to this data, which not only serves to gain knowledge in the field of earth system research, but is also combined with data from the social sciences, for example, in order to gain completely new insights, for example into the social effects of climate change. This is called data-intensive science. It is a new, independent component in computing in science. Computer science enables the efficient storage, analysis and graphical representation of data and transforms it into information and ultimately into new scientific findings.
Modelling, Simulation and Visualization
Using state-of-the-art spectroscopy methods, we can penetrate the structural cosmos down to the atomic detail. Many thousands of structures available today, especially of the essential functional carriers in living organisms, proteins, form the basis of modern molecular biological research. Computers not only play an important role in structural elucidation, they are used in many different ways for modeling, simulation and visualization. For example, computer models characterize the flexibility and stability of molecules and enable the prediction of interactions between them. For example, in pharmaceutical research, computer-assisted active ingredients are being developed for new drugs.
Scientific Computing and Visualization
The simulation of physical phenomena with high temporal and local resolution requires computing power that can only be achieved by using parallel computer architectures and special processors. For the numerical analysis and visualization of the resulting, rapidly growing amounts of result data, innovative methods are required that do not impair the massively parallel calculation. For example, techniques for extracting 3D graphics from spatially distributed raw data - e.g. temperature, humidity and currents - are developed and executed on the supercomputer together with the simulation. The results prepared in this way are then made available as a 3D film in a virtual reality system using streaming methods, in which complex interrelationships can be explored interactively.
Especially in the sciences there is a great need for methods that can discover interesting information in the flood of data with complex contexts. Machine learning is a young discipline that develops tools for this purpose. It combines methods from computer science (algorithms for processing large amounts of data) with statistical methods (calculation of quality guarantees for the results found). The methods developed in this way are increasingly being used to analyze scientific data, for example in biology, the neurosciences, medicine or physics.