DataPreTest, DatauntilSeptember, Datafinal_2: These or similar folder designations can be found on many scientists' computers. But which of this data is actually relevant for an article publication? Does the knowledge about it still exist three years later? Does my data management comply with the rules of Good Research Practice? Is it possible to share data in such a structure with colleagues?

Research data management (RDM) starts at the beginning of data acquisition and on your own computer. In order to learn the most important techniques of RDM on your own computer and to avoid data chaos, the working group Scientific Computing offers a workshop on research data management.

The workshop consists of content-related discussions that link the principles and techniques taught with the participants' everyday scientific work, as well as some practical parts in which the methods learnt are to be applied to a realistic data set. In this way, the participants understand what is important, what difficulties poor data management can cause and how to prevent this. The aim of the practical elements is to convert a dataset into a publishable format and treat it in accordance with the FAIR principles.

• Course organisation
  • 2 half days (5 - 6h) course
  • Practical part and discussion
• Topics
  • RDM basics, FAIR principles, metadata, standardisation, storage, UOL systems
  • Legal aspects, personal data
  • Open Science, Error management
  • Data publication, data repositories, Dare (UOL data repository)
• Target audience
  • Anyone who collects their own research data (especially PhD-students)
• Dates
  • 13.02.2025 09:00 – 15:00  & 27.02.2024 09:00 – 15:00
  • Registration via Stud.IP

Errors in science carry the stigma of incompetence and sometimes even fraud. Many scientists fear the loss of their reputation if errors are found in their work. The handling of errors in academic institutions such as universities is often characterized by fear on one hand and blame on the other. Does the fear of losing one's reputation lead to errors being concealed? Is this also a reason why Open Science practices are only slowly being adopted into the scientific system?

To shift our approach to errors from a culture of blame to a more constructive view, errors must be openly addressed. If the impression arises that errors only happen to "bad" scientists, errors will be hidden, covered up, downplayed, and responsibility pushed away. This leads to distrust in scientific work and delays progress.

In this course, I would like to provide an insight into the current handling of errors in academia. I aim to prepare participants for the fact that errors in science are inevitable and that we must normalize how we deal with them. Together, we will cover the following topics:

• Course Format:
  • 3-hour course in a seminar atmosphere
• Topics:
  • Distinguishing between honest error, fraud, and questionable research practices
  • Current methods for addressing errors in published works
  • Error culture in academia and other areas of life
  • Open Science as an opportunity to improve the handling of errors
• Target Audience:
  • Everyone working in academic science
• Dates:
  • March 25, 2025, 09:00 – 12:00
  • Registration via Stud.IP