Michael Stifel Center Jena
for Data-Driven & Simulation Science
The aim of the center is to promote interdisciplinary research and teaching in the field of data-driven and simulation-based sciences, primarily with connecting factors to the natural sciences (especially earth sciences, biology, chemistry, physics and medicine) as well as the mathematics and computer science, including the social and behavioral sciences and the humanities.
The three thematic pillars
In the past, scientific progress was made exclusively on the basis of experiments and observation as well as theoretical reasoning. The advent of powerful computers has led to an extension of this traditional epistemology by two novel paradigms: data-driven science, succinctly summarized under the motif Knowledge from data, and simulation science, based on the principle Data from knowledge. The Michael Stifel Center Jena for Data-Driven and Simulation Science – in the following, MSCJ – builds upon these paradigms and combines them into a common structure. It regards the potential for connecting the two innovative areas as a highly promising topic and intends to use the synergistic effects resulting from the complementary competencies of the two paradigms in an optimal way. The combination of data-driven and simulation science gives rise to a tripartite structure of the MSCJ, with the components ‘Data-Driven Science’, ‘Simulation Science’ and ‘Data-Model Integration’. This tripartite structure, to be described further below, underlines the significance of each of the areas.
Data-driven science
The young paradigm of data-driven science uses the most recent advances in the storage, analysis and visualization of large amounts of data to derive hypotheses in a target-oriented way, thus arriving at new insights. New technologies and measurement techniques have made this modern form of research possible, as ever growing amounts of observational and experimental data can be collected or generated, with lesser human intervention and at rapidly decreasing cost, without having to specify the exact purpose of the data beforehand. The possibilities of analyzing such often heterogeneous data are afforded by the use of powerful computers and methods from information technology. Pertinent examples range from the “geographical distribution and functional traits of species”, “satellite remote sensing observation”, “data from telescopes with extremely high resolution”, “large-scale sensor networks for the detection of materials cycles and traffic density” to “sequencing data”, “imaging methods in medicine” and “user data from social networks”.
Model-data integration
The research fields of data-intensive and simulation science outlined above, while acting in complementary ways, can cross-fertilize each other when they are closely and adequately connected. The epistemological potential of this approach – generating knowledge from data as well as data from knowledge (in particular, through modelling), is one of the most fundamental challenges faced by contemporary sciences. Interdisciplinary research and cooperation is indispensable for such a complex process of knowledge generation. It includes the experimental collection of data and their exploration and analysis, followed by modelling, simulation, the data-driven parameterization of the models as well as their revision on the basis of the results obtained in simulation, and the comparison of these results with observed data. The entire range of scientific disciplines represented in and around Jena takes on these multiple challenges in pursuing the programme of generating Knowledge from data and Data from knowledge.
Simulation science
Model-based computer simulation plays a central role in many domains of the natural sciences and engineering as well as, increasingly, in the Life Sciences and the Social Sciences. Examples are provided by Computational Physics, Computational Chemistry, Computational Biology and Computational Neurosciences. On the basis of mathematical models, the computer allows us to explore complex systems which could not be described otherwise, and whose experimental investigation would involve considerably higher costs. Iterative model building, in interaction with simulations, requires the development of sophisticated methods, concepts and tools for the entire research process. Modern working and research environments use simulations not only on high-performance computers, but on all types of computational architectures, even on moderately powerful desktop computers. Model building, implementation, verification and validation are the central topics of simulation science.
