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The Data Train Team


The Research Data Working Group of the U Bremen Research Alliance monitors the development of the training curriculum.


Data Train is in line with efforts of the National Research Data Infrastructure NFDI-consortia (funded by the DFG) which consider education as a cross-cutting topic. On the one hand, institutions from Bremen which are part of funded NFDI-consortia contribute to the Data Train progam. On the other hand, the Data Train concept and curriculum is shared with these consortia.


Data Train further operates in close collaboration with and is supported by theData Science Center(DSC) of the University of Bremen which defined "Qualification" as one of its three pillars (i.e. Qualification, Services, Research). The DSC brings together scientists from all faculties of the University of Bremen to initiate scientific cooperation and promote the development of novel research questions related to data science.


With BYRD (Bremen Early Career Researcher Development), the firmly established support center for early-career researchers at the University of Bremen, Data Train has an experienced collaboration partner with an excellent network at the University of Bremen. Both programs benefit from the clearly-defined foci and well-coordinated collaboration.


Data Train collaborations


Iris Pigeot

Prof. Dr. Iris Pigeot

Co-Chair U Bremen Research Alliance

Chair Research Data Working Group

Co-Speaker for NFDI4Health


Frank Oliver Glöckner

Prof. Dr. Frank Oliver Glöckner

Chair Research Data Working Group

Speaker for NFDI4Biodiversity


Rolf Drechsler

Prof. Dr. Rolf Drechsler

Spokesperson for the Data Science Center of the University of Bremen



Tanja Hörner

Dr. Tanja Hörner

Coordinator Data Train

Coordinator Research Data Working Group and associated exchange groups


Leonie Lenz-Seraphin

Leonie Lenz-Seraphin

Student assistant coordination Data Train

Student of the master program medical biometry/biostatistics at Bremen University

Lecturer Team

A team of enthusiastic lecturers from different institutions within the U Bremen Research Alliance build up this joint curriculum!

Prof. Dr. Benedikt Buchner

Benedikt Buchner is Professor of Civil Lawand the director of the Institute for Information, Health and Medical Law (IGMR) at the University of Bremen.

Research focus on data protection and information law. Research depends on the free use of research data while the (exclusivity) interests of those whose personal data are to be processed for research purposes or those who own the copyright for study or similar data are opposed to this. It is the task of the law to resolve this conflict.

RDM is an important as well as difficult challenge, which can only be met if all the disciplines concerned closely cooperate.

  1. Starter Track: Data protection and licenses

Prof. Dr. Thorsten Dickhaus

Thorsten Dickhaus is Professor for Mathematical Statistics at Faculty of Mathematics and Computer Sciences (FB 3) at the University of Bremen.

Thorsten Dickhaus studied mathematics in Aachen and Düsseldorf, and he received his Dr. rer. nat. degree in mathematics and application areas from Heinrich-Heine-University Düsseldorf in 2008. Afterwards, we worked in Berlin, first as a PostDoc at the Berlin Institute of Technology, then as a junior professor at the Humboldt-University and finally as a scientific staff member at the Weierstrass Institute for Applied Analysis and Stochastics. Since March 2015, Thorsten Dickhaus is Full Professor and Head of the Working Group “Mathematical Statistics” at Faculty 3: Mathematics and Computer Science at the University of Bremen. Since 2018 he is the Vice Dean of Academics of the Faculty of Mathematics and Computer Science. His research interests include the development of statistical methods and their applications, in particular to high-dimensional and complex structured data from the life sciences and from economics.

I want to contribute to the Data Train education program for a better mathematical understanding about data science applications and, because data science crucially relies on the interdisciplinary exchange of ideas and competences

  1. Data Scientist Track: Quantitative analyses for data science

Prof. Dr. Vanessa Didelez

Vanessa Didelez is Deputy Head of the Department of Biometry and Data Management at the Leibniz-Institute for Prevention Research and Epidemiology - BIPS and Professor of Statistics with focus on Causal Inference at the University of Bremen.

Prof Didelez research deals with the statistical modelling of and methods for data analysis. Specifically, she aims at developing statistical approaches to address questions about the consequences of (possibly hypothetical) interventions, e.g. by how much would a given increase in physical activity reduce childhood obesity? This kind of inference is known as causal inference. The particular statistical challenge consists of adequately addressing weaknesses and limitations of the data, such as lack of, or imperfect, randomization, systematic selection or drop-out etc. These need to be accounted for by suitable models and methods. A key prerequisite is the in-depth understanding and scrutinizing of the underlying assumptions so that these can be made plausible, either empirically or based on subject matter knowledge. Furthermore, Prof Didelez focusses on time-structured data, such as cohort data or event-history or survival data. She has developed new approaches for causal path analysis, dynamic graphical models and causal discovery. The new methods have applications in epidemiology or public health, such as for analysing the causes and effects of childhood obesity, or for cancer prevention or dementia research.

I believe that data science can make an enormous contribution to evidence-based decision making. This requires the ability to carefully and critically analyse data, which is what I strive to teach on the Data.

Dr. Martin Dörenkämper

Martin Dörenkämper is a researcher at the Fraunhofer institute for wind energy systems IWES, Department of Aerodynamics, CFD and Stochastic Dynamics, Oldenburg

Martin Dörenkämper has been working on evaluation of weather and wind farm data in the context of wind energy research since more than 10 years. After his undergraduate and graduate studies in meteorology at the Universities of Hamburg and Oklahoma, his PhD research at the University of Oldenburg focused on energy meteorology. His current research addresses the improvement and validation of industry-suited models for wind energy siting and wind farm yield analysis applications. This work includes working with multi-dimensional as well as time-series based data of various complexity and confidentiality levels. At Fraunhofer IWES Martin coordinates joint research projects with industrial and academic partners.

Within Data Train, we would like to share our experience from applied R & D and work with confidential data especially in close exchange with the industry.

  1. Starter Track: Managing confidential data

Prof. Dr. Rolf Drechsler

Rolf Drechsler received the Diplom a and Dr. phil. nat. degrees in computer science from the Johann Wolfgang Goethe University in Frankfurt am Main, Germany, in 1992 and 1995, respectively. He worked at the Institute of Computer Science, Albert-Ludwigs University, Freiburg im Breisgau, Germany, from 1995 to 2000, and at the Corporate Technology Department, Siemens AG, Munich, Germany, from 2000 to 2001. Since October 2001, Rolf Drechsler is Full Professor and Head of the Group of Computer Architecture, Institute of Computer Science, at the University of Bremen, Germany. In 2011, he additionally became the Director of the Cyber-Physical Systems Group at the German Research Center for Artificial Intelligence (DFKI) in Bremen. His current research interests include the development and design of data structures and algorithms with a focus on circuit and system design. He is an IEEE Fellow.

From 2008 to 2013 he was the Vice Rector for Research and Young Academics at the University of Bremen. Since 2018 he is the Dean of the Faculty of Mathematics and Computer Science. He is one of the founders and currently the spokesperson of the Data Science Center at University of Bremen (DSC@UB).

  1. Starter Track: Computer science basics for data science

Dr. Christian Fieberg

Christian Fieberg is Researcher for the field of business administration, especially empirical capital market research and derivatives at the Faculty of Business Studies and Economics at the University of Bremen.

Working with large data sets, the use of complex methods (especially from the areas of statistics, econometrics, optimization, operations research, simulation and machine learning), the use of statistical software (especially Matlab / Octave / Freemat, R, Stata, Python, Excel / VBA) and the transfer of research results to software tools that can be used in practice.

I am interested in a strong collaboration between all U Bremen Research Alliance members to drive qualification in research data management and data science forward.

Prof. Dr. Frank Oliver Glöckner

Frank Oliver Glöckner is Professor for Earth System Data Science at the Department of Geosciences at the University of Bremen. He is head of Data at the Computing and Data Center of the Alfred Wegener Institute Bremerhaven and Adjunct Professor for Bioinformatics at the Jacobs University Bremen. He is head of the Data Publisher for Earth and Environmental Science PANGAEA at MARUM and speaker of the NFDI4BioDiversity consortium. His interdisciplinary team of geologists, biologists, engineers, and software developers located at the AWI and MARUM has a national and international proven track record in research data management, data logistics and data science.

In the U Bremen Research Alliance he is interested in a strong collaboration between all members to drive qualification in research data management and data science forward. He will contribute his network and experience to establish the city and state of Bremen as a center of excellence in these fields.

  1. Starter Track: Data and information management
  2. Data Steward Track: Tools for FAIR data handling

Dr. Julia Gottschall

Julia Gottschall is Chief Scientist at Fraunhofer Institute for Wind Energy System IWES, Section Wind Farm Development, Bremerhaven / Bremen.

Julia Gottschall has been working on the acquisition and evaluation of wind data in the context of wind energy research for more than 15 years. After completing her doctorate in applied physics at the University of Oldenburg, she worked at various research institutes in Germany and abroad, particularly in applied and industry-related research and development. Her work includes the application of mathematical models for the reconstruction of wind field parameters as well as the management of complex data sets, which are often subject to confidentiality requirements. One focus of her work is the application of wind lidar measurement technology, which in recent years has provided a completely new approach to the description of wind fields relevant to wind energy use and at the same time represents a new challenge with regard to the handling of the data obtained in this way. Julia Gottschall represents IWES on these topics in various international committees (including IEC – International Electrotechnical Commission, IEA Wind TCP – International Energy Agency Wind Technology Collaboration Programme).

Within Data Train, we would like to share our experience from applied R&D and work with confidential data especially in close exchange with the industry.

  1. Starter Track: Managing confidential data

Dr. Antonie Haas

Antonie Haas





Antonie Haas is Senior GIS Scientist.

Scientific data and research results are often understandable for experts only. Visualization transforms numbers in symbols or graphics and has the ability to decode complex interrelations even to non-experts.

Data-driven science is an ongoing process and requires beside excellent scientific expertise, expertise in standardized data management as well as the knowledge and application of analyses methods to work with high data volumes (e.g. Big Data). The training of required capabilities is key to excellent scientific results, and focus of the U Bremen Research Alliance training program.

  1. Data Scientist Track: Data visualization
  2. Data Scientist Track: Mapping

Dr. Jan-Ocko Heuer

Jan-Ocko Heuer is Postdoctoral Researcher at Research Data Center (RDC) Qualiservice and SOCIUM Research Center on Inequality and Social Policy at University of Bremen.

He is a sociologist (Dipl.-Soz.) who obtained a PhD in Economics and Social Sciences (Dr. rer. pol.) in 2014 from the Bremen International Graduate School of Social Sciences (BIGSSS) at the University of Bremen. He has worked as a postdoctoral researcher in several international research projects at the University of Bremen and the Humboldt-Universität zu Berlin and published extensively on the topics of social policy and consumer bankruptcy. Since 2018 he works as a domain expert for social sciences at the Research Data Center (RDC) Qualiservice at the University of Bremen. At Qualiservice he is responsible for various aspects of data management and data curation and he is teaching in the areas of empirical research methods and data management. Since January 2021 he is also coordinating the measure “Generation of qualitative data – RDM portfolio für qualitative social research” as part of the KonsortSWD within the National Research Data Infrastructure (NFDI).

A proper understanding of research data management and data science is nowadays essential for researchers in all scientific disciplines. I want to contribute my knowledge and experiences from social science research and the management of (qualitative) research data to offer a fundamental education in those fields for young researchers.

  1. Starter Track: Managing qualitative data

Prof. Dr. Betina Hollstein

Betina Hollstein is Professor for Microsociology and Qualitative Methods at University Bremen. She is head of QUALISERVICE, national data service center for social science qualitative research data, located at the SOCIUM – Research Center at University of Bremen.
She is member of the German Data Forum (RatSWD), advisory council to the federal government, and co-spokesperson of the Consortium for the Social, Behavioural, Educational, and Economic Sciences (KonsortSWD).

Within Data Train she is interested in fostering interdisciplinary bonds in research data management and data science across different methodological approaches and data types, with special emphasis on sensitive personal data and research ethics.

  1. Starter Track: Managing qualitative data

Prof. Dr. Dieter Hutter

Dieter Hutter received his PhD from the University of Karlsruhe working on automating inductive theorem proving. In 1991 he moved to the Saarland University and joined the German Research Center for Artificial Intelligence (DFKI) in 1993. He guided various projects in Formal Methods and Security. Moving to Bremen in 2008, Dieter Hutter is now vice director of the Cyber-Physical-System Department at DFKI and honorary professor at Bremen University. He was co-initiator of the German DFG Priority Program on Reliably Secure Software Systems and speaker of the section on Formal Methods and Software Engineering for Safety and Security in the German Informatics Society. He is working in the areas of security, formal methods and change management. In particular, he is working on structuring mechanisms for information flow control supporting a formal notion of security in the large.

Basic knowledge of IT security and data protection has become an indispensable part of a computer science education in recent years. This course is designed to provide an introduction to this domain.

  1. Starter Track: Cryptography basics
  2. Starter Track: Security & Privacy

Dr. Dennis-Kenji Kipker

Dennis-Kenji Kipker is Research Manager at the Institute for Information, Health and Medical Law (IGMR) situated at the University of Bremen and board member of the  European Academy for Freedom of Information and Data Protection (EAID) located in Berlin.

Data security is one of the central requirements and at the same time challenges for the secure handling of research data. In this context, my research focuses on the legal requirements for secure data storage and management principles to be implemented accordingly. RDM is an important as well as difficult challenge, which can only be met if all the disciplines concerned closely cooperate.

My goal is to create a higher level of data security in research contexts.

  1. Starter Track: Data protection and licenses

Prof. Dr. Kristina Klein

Doctoral Degree (Dr.’in rer. pol.) with distinction (summa cum laude), University of Cologne
Post-Doctoral Researcher, Department of Marketing and Brand Management (Prof. Dr. Franziska Völckner), University of Cologne
W2 Professor of Business Administration, particularly Marketing and Consumer Behavior (non-tenured) at the Faculty of Business Studies & Economics at the University of Bremen

My research is empirical-quantitative, i.e., in all my projects I work with data (primary or secondary data).

  • Digital technologies to improve customer experience (gamification)
  • Digital technologies for customer interaction (chatbot design, voice applications)
  • Serious games (in employer branding)
  • Influencer Marketing
  • Sensory marketing and emotions
  • Brand Activism

To deal with data, to collect data oneself and to know exactly, what one is doing, is the basic requirement for science and business in the future. Therefore, I contribute to “Data Train” with pleasure, supporting doctoral students along the way.

Dr. Nikolay Koldunov

Nikolay Koldunov

Nikolay Koldunov is Scientist at Alfred Wegener Institute.

Very high-resolution ocean and climate modelling. Pre- and post processing of large amounts of geophysical data. Interactive data analysis and visualization.

Data literacy is necessary to do most of the science nowadays, but this is not something most of us learned in the University. I believe that the new generation of scientists should be given an opportunity to quickly and efficiently acquire information about different aspects of data related topics, select what is useful for their research and build further self-education on this solid basis. This will leave more time for doing science on the one hand and help to open new scientific directions on the other. I hope my experience on working with large amounts of data will be useful for others, and I am also going to use this opportunity to learn from fellow lectures and students.

Dr. Ivaylo Kostadinov

Ivaylo Kostadinov is Technical Manager (NFDI4Biodiveristy Research Data Commons).

I spent the last seven years building a service-oriented infrastructure for supporting scientists in Biodiversity and Ecology with their data management tasks. I co-designed a unified data submission interface, established and coordinated a Help Desk, personally curated molecular sequence datasets and supported the preparation of Data Management Plans.

I want to change the perception of Research Data Management from necessary evil to something worth doing.

  1. Starter Track: Data and information management

Prof. Dr. Iris Pigeot

Professor Iris Pigeot has been the director of the today’s Leibniz Institute for Prevention Research and Epidemiology – BIPS since March 2004 and has been in charge of the Department of Biometry and Data Management of the institute since September 2001. Furthermore, she has been professor for Statistics with a Focus on Biometry and Methods in Epidemiology at the University of Bremen since 2001. Since 2019, Iris Pigeot has been chairwoman of the U Bremen Research Alliance together with Bernd Scholz-Reiter (president of the University of Bremen). She initiated the interdisciplinary graduate education program Data Train on “Research data management and data science” in 2019 to serve the upcoming needs in this area.

This education program is led by Iris Pigeot together with Frank Oliver Glöckner from theAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research As Co- Spokesperson of the consortium to set up a National Research Data Infrastructure for Personal Health Data (NFDI4Health), she links the graduate education program to this German - wide initiative and ensures the implementation of uniform standards for personal health data.

  1. Starter Track: Statistical thinking

Prof. Dr. Dr. Norman Sieroka

Norman Sieroka is Professor for Philosophy at the University of Bremen. He is a member of the Directory Board of the Turing Centre Zurich and of the Governance Board of ETH’s "Rethink" initiative (rethinking design with artificial intelligence). He studied philosophy, physics, and mathematics in Heidelberg and Cambridge. In fact, a special trait of his research group is that all members have backgrounds in more than one academic discipline. The group is interested in questions about "how science works" and w hat values are pursued in science. Here special attention is paid to the role played by data and by artificial intelligence within different disciplines (such as physics and pharmaceutical science) and different research contexts (such as theory development, hypothesis generation, and problem solving).

Being born and raised near Bremen, Norman Sieroka is happy to be on board with the U Bremen Research Alliance's program Data Train and to make the region a haven for deliberate data science.

  1. Starter Track: Philosophical reflections on data science

Dr. Brenner Silva

Brenner Silva is Software Engineer and Research Scientist at the Alfred-Wegener-Institute, Bremerhaven. He is with the Alfred-Wegener-Institute and takes part in the development of the framework “Observation to Archive and Analysis” (O2A) within the scope of the “Digital Earth” project. He is a data scientist and contributes with the development of applications for data and metadata management, in particular data harmonization and quality control. From the scientific instrumentation to the data-driven application, his interests are in building a research data infrastructure that makes research efforts and applications more efficient and sustainable. Brenner Silva is experienced in data collection and in development of methods for data analysis and management. Particularly in the Earth and the Environment research field, he is interested in near real-time monitoring and automatic processing of temporal and spatial data, as well as in developing analytical workflows and integrating data services.

The data system essentially requires the human and digitalization is a mind-driven process. With the Data Train, I would like contribute to our understanding and better use of existing technologies in data provision.

Björn Tings

Björn Tings accomplished his Bachelor studies in Scientific Programming and his simultaneous qualification in Mathematical-technical Software Development in 2010 at RWTH Aachen University. In 2013 he received his Master degree in Artificial Intelligence at Maastricht University.

Since 2013 he is employed as research associate in the team of Synthetic Aperture Radar (SAR) oceanography at the Remote Sensing Technology Institute of German Aerospace Center (DLR) in Bremen, Germany. He is responsible for integrating the team’s research and development work into operational prototype software for robust and fast processing of SAR data.

His research comprises the automatic detection and classification of ship signatures on SAR imagery. As PhD student at Helmut Schmidt University, Hamburg he also elaborates on the automatic recognition of ship’s wake signatures of moving vessels.

  1. Starter Track: Data science and big data
  2. Data Steward Track: Data preparation

Prof. Dr. Hans-Christian Waldmann

University of Bremen, FB11 :: Department of Psychology :: Theoretical Psychology & Psychometrics

(a) Advanced statistical norming procedures in psychological testing, SAS analysis macro/batch automatization, database design for health care studies, data documentation, pre-analytical data handling and its impact on results,

(b) philosophy of science (especially: the qualitative aspects of quantitative data, concepts of probability, paradigms in science and statistics)

25 years in thestatistical consulting business have taught me this: prevention is better thanrehabilitation. Most projects that went astray did not so because of pitfalls in data analysis or lack of programming skills, but, aside from ill project management, for reasons of negligant data handling prior to statistical modeling, and, most important, drawing unwarranted conclusions from data. Meaning is not inherent to data, it is attached to it by researchers, and in order to“do it right” one must be aware of the patterns behind “doing it wrong”.

  1. Starter Track: About the meaningfulness of data

Dr. Max Westphal

Max Westphal is a postdoc for Data Science and Biostatistics at the Fraunhofer Institute for Digital Medicine in Bremen. Beforehand, in 2019, he completed his PhD on the topic “Model Selection and Evaluation in Supervised Machine Learning“ within the DFG-funded research training group  π³ at the University of Bremen. His research is concerned with medical diagnosis and prognosis applications, in particular with statistical methods for the evaluation of medical tests and AI-based prediction models. At Fraunhofer MEVIS he also contributes to different applied research projects by developing statistical and predictive models, for instance to enable innovative clinical decision support systems.

I am looking forward to contribute to the cross-disciplinary Data Train program which will help to connect and impart all the important concepts from the diverse field of Data Science.