JProf. Hermann, why is the topic of Open Science so important to you?

I think open science - i.e. the disclosure of the research process - is very important for several reasons: in terms of accountability, I am particularly concerned that we stand by our scientific results and give others the opportunity to check these results. In computer science, it was possible for a long time to verify results solely based on publications, i.e. without having to know the underlying research artifacts. However, this is becoming increasingly difficult as these research artifacts become more and more complex. In my opinion, it is therefore important to pursue and promote open science, because otherwise the peer review process, which is so far based on publications, will eventually reach its limits. In addition, the peer review system, i.e. the mutual review of scientific findings, is essential in science. On websites such as "Retraction Watch", which provide information about withdrawn articles, we can see that the number of withdrawals has already increased. Incidentally, this does not always have to be deliberate falsification; things can also be overlooked and mistakes made in the scientific process. Such errors are not necessarily always recognized during the peer review of the publication itself, as it is often not possible to follow the process in every detail. This is why it is so important to be open as a scientist and not to choose the publication as the only means of communication, but also to provide all other sources that you have created (e.g. research data, codes, prototypes, etc.). If we do not make research data traceable, I believe that the trust placed in science can no longer be justified. That is why the topic of open science is so important to me.

How do scientists benefit from sharing research data?

Publicly accessible research artifacts offer the opportunity to build on existing research data, prototypes and approaches with your own research. This allows us to gain new insights more quickly. If these findings and the process are also reviewed externally, we have a strong basis on which we can continue to build scientifically. In empirical social research, for example, entire interview methodologies and questionnaires are shared. These are interesting artifacts, i.e. information on which you can then base your own research.

However, many of your colleagues are skeptical about sharing research data and methods because it is very time-consuming. How do you see that?

My colleagues are absolutely right. Although there are now ways to make your own data available, it is still quite complicated in many cases and it is particularly difficult to prepare the data in such a way that it is available and understandable for everyone. The big challenge is therefore to develop methods to document software, data sets and data analyses in a comprehensible way and make them reusable - across different infrastructures. We have made great progress here in recent years and I am very confident that sooner or later we will find methods to simplify the provision of research data. However, it is also crucial that researchers are willing to publish their own data sets - and not just the final publication. In many cases, the hurdle is not as great as one might think. The mere provision of a dataset is often enough. You do not have to document and process it in all its detail. Many scientists around the world are grateful if they can find anything at all to fall back on and take the time to understand the data.