Event Calendar

Are you open to Open Science? Do you want to inform yourself and join the discussion about what Open Science has to offer and where the limits lie? “Meet, Share, Inspire, Care!” – under the motto we picked up from the last Open Science Festivals in Hannover and Amsterdam, there will be a colourful mix of current topics and formats around Open Science.

Students will learn how to develop and use predictive models (discrete choice models) in the software R and how to introduce such models in mathematical models for decision-making (i.e., mixed integer programs) to consider demand as an auxiliary variable. The models will be implemented in a modeling environment (GAMS). Case studies will be used for practicing purposes.

BEXIS2 is a web-based data management system designed to facilitate the management and sharing of scientific data in collaborative projects. The platform provides a set of tools and features to support the collection, curation, and sharing of scientific data, including quality and access control, and ways to describe data structures in detail. It also provides a range of interfaces for accessing and editing data and metadata. Due to its modular design, the system can be easily extended and adapted, making it very flexible to use. BEXIS2 is an open-source software that is continuously developed and improved by a small community of developers. It is used in a number of collaborative projects (e.g. DFG Priority Programs, Collaborative Research Centers, Clusters of Excellence) in various disciplines (e.g. ecology, biodiversity research, soil sciences, agricultural research).

Open access has developed into a highly dynamic field in the area of book publications in recent years. As the publication landscape in the area of scholarly books is very heterogeneous, the established open access models for journals cannot simply be transferred. An efficient infrastructure with various publication and funding options has now also developed in the area of open access books. Get to know your options for publishing your open access book!

After completing this course, students will be able to define and describe endogeneity problems in different empirical settings, they will have a better understanding of whether and when causal identification is possible how to implement techniques that address endogeneity, and they will be aware of the (dis)advantages of different methods.

After completing this course, students will be able to define and describe endogeneity problems in different empirical settings, they will have a better understanding of whether and when causal identification is possible how to implement techniques that address endogeneity, and they will be aware of the (dis)advantages of different methods.