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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.



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!

Dr. Daniel Otero Baguer

Post-doctoral researcher, head of digital pathology group at the Faculty Mathematics and Computer Science, University of Bremen.

Research interests:

  • Inverse problems
  • Machine learning
  • Image and signal processing
  • Computational engineering
  • Parameter indentification
  • Computational pathology

"I enjoy showing young researchers the amazing field of deep learning and its amazing applications. Also, I always try to find the easiest way to for students to learn the concepts, and this is often times a great challenge."

  1. Data Scientist Track: Deep learning

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.

"Research Data Management 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

Dr. Arjun Chennu

Arjun Chennu

Arjun Chennu is Group leader of the team Data Science and Technology at ZMT Bremen.

Our research explores ways to leverage the diverse and rapidly growing techniques in data science, machine learning and statistical modeling towards data-driven analytics for the benefit of tropical marine sciences. Our team bring together expertise across domains of habitat mapping, computer vision, machine learning, neural networks,  biogeochemistry, benthic ecology, probabilistic analyses, stakeholder utility networks, software development, optical physics, sensor systems and platforms, engineering of data acquisition and analytical workflows.

"Data is the currency of knowledge in the 21st century. We need to invest this currency better to manage and grow our wealth of knowledge."

  1. Data Steward: Reproducibility in science: How and why?

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 Train."

1. Starter Track: Asking the right research questions in data science

2. Data Scientist Track: Causal learning

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

PD 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."

  1. Data Steward: Getting started in R
  2. Data Steward: Erste Schritte mit MATLAB

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 I would like 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: How to write a data management plan?

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. Nikolitsa Grigoropoulou

Nikolitsa Grigoropoulou is a sociologist with a background in social psychology. She received her Ph.D. in sociology from the University of North Texas in 2019, with the support of the Fulbright Foundation and the Institute of International Education. Substantively, her research is focused on interreligious communications online, (ir)religious minorities, xenophobia, and social inequalities writ large. Methodologically, she is trained in quantitative empirical methods, big data analytics, and computational social science, particularly text analytics, such as text classification, topic modeling, and sentiment analysis. At SOCIUM, she is currently working on the research project “Large-scale data and field research in the study of social networks,” linking quantitative and qualitative social science methods with big data to address methodological issues with big data and improve research inferences.

"Data scientists and social scientists have a few established venues of communication. As a result, there are limited opportunities for collaboration and cross-fertilization. Computational social science acts as a bridge between fields. I believe it is necessary to promote it among young scientists."

  1. Data Scientist Track: Computational social sciences

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: Visualization in science: Principles & critical reflections
  2. Data Scientist Track: Visual analytics using GIS

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
  2. Data Steward: Data preparation

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 I am 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
  2. Data Steward: Data preparation

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. James Imber

James Imber

James Imber is employed by the Remote Sensing Technology Institute of the German Aerospace Center (DLR) as a member of the Synthetic Aperture Radar (SAR) Oceanography group.

His work involves the application of machine learning techniques to extract information from SAR images of the ocean surface.

"Data has become ubiquitous and the ability to handle data in a variety of forms and contexts is a valuable skillset for everyone."

  1. Data Steward Track: Data preparation

Prof. Dr. Dennis-Kenji Kipker

Dennis-Kenji Kipker is Professor for IT Security Law at the HSB City University of Applied Sciences, 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."

  1. Data Steward Track: Data extraction from external online platforms using R

Dr. Nikolay Koldunov

Nikolay Koldunov

Nikolay Koldunov is Scientist at Alfred Wegener Institute.

He works on very high-resolution ocean and climate modelling, pre- and post processing of large amounts of geophysical data and 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."

  1. Data Steward Track: Getting started with Python
  2. Data Steward Track: Data preparation

Karl Kortum

Karl Kortum


karl.kortumprotect me ?!dlrprotect me ?!.de​​​​​​​


Karl Kortum is PhD Candidate at the German Aerospace Center (DLR).

Development of autonomous algorithms for the characterisation and discrimination of arctic sea ice using satellite-borne radar systems, as well as the fusion of data from various instruments towards purely data driven analysis and the extrapolation of ground measurements to the scope of satellite acquisitions.

"Data and its derivative information are exploding resources in times of rapid change in climate and society. Presented with a mounting challenge of data management as well as the opportunity of exploitation, I believe it is fruitful to invest in researchers able to assess and navigate these new waters to tackle the obstacles that lie ahead."

  1. Data Steward Track: Data preparation

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
  2. Data Steward: How to write a data management plan?

Jimena Linares

Jimena Linares

Jimena Linares works as Data curation services in the submission service, Support in Research Data Management, and Helpdesk and user suppor.

Data have a tremendous potential, meaning the data we produce can provide diverse benefits and should be shared. However, we have to reconsider the way we interact with the data we produce so they are shared in an optimal way. I am interested in 1) optimizing the relationship we have with data and 2) exploring how to represent data so they are made FAIR to everyone.

"My motivation is to highlight the importance of Research Data Management in any discipline, independently of one’s academic stage or working position."

  1. Data Steward: How to write a data management plan?

Prof. Dr. Sebastian Maneth

Sebastian Maneth is  Heisenberg Professor (full professor) University of Bremen.

Sebastian Maneth's research interest lies in efficient storage and querying technology for semi-structured data. He is developing novel methods to compress graph structured data and methods that make possible to directly execute queries on compressed data, without prior decompression. Since recently he is also working on analysing eye tracking data using machine learning methods, for instance for biometrics.

"To meet interested students and teach them about fascinating topics related to relational database management systems and database query languages."

  1. Data Steward Track: Data base skills

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 and Rolf Drechsler. 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, I am 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
  2. Data Scientist Track: Visualization in science: principles & critical reflections

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."

  1. Data Steward Track: Data provisioning

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.

"I would like to contribute and share my expertise in artificial intelligence in the frame of the Data Train program."

  1. Starter Track: Data science and big data

Prof. Dr. Hans-Christian Waldmann

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

a) Advanced statistical procedures in psychological testing, SAS analysis macro/batch automatization, database design for health care studies, (b) philosophy of science and epistemology, (especially: the qualitative aspects of quantitative data, concepts of probability, paradigms in science and statistics), foundational issues in Psychology, mind-body-problem and concepts of soul.


>Instead of concentrating upon probing the nature of the data and upon the questions that concern scientists, statistical practioners have been prone to commit errors of the third kind - that is, giving exact answers to the wrong questions - which is perhaps the most serious of the three kinds of errors.

Tukey (1962), zit in: Clark, C.A. (1963). Hypothesis testing in relation to statistical Methodolog (p.469). Review of Educational Research, 33, 455-473.

"Since, as a psychologist and statistician, I cannot claim expertise in your respective field of work, I will not, and cannot, tell you how to “do it right”. But the patterns behind „doing it wrong“ are quite universal: unawareness and intransparency. My aim is to shed some light onto the implicit and hidden presuppositions in data science and foster a critical mindset when it comes to relating data to meaning in your specific discipline."

  1. Starter Track: About the meaningfulness of data

Tanja Weibulat

My research interests are biodiversity informatics, database management, scientific data curation and project management. In the last 13 years I have been able to observe and accompany the important role that scientific data management is increasingly playing in my work with scientists and collection curators in the field of biology. I was able to help ensure that data is handled according to the FAIR principles through support and training offers when working with the data management software Diversity Workbench as well as the joint development of work and data flows along the data life cycle in the infrastructure project "German Federation for Biological Data" (GFBio) and made available for re-use.

"The importance of research data management continues to grow in all scientific disciplines. I would like to convey the importance and benefit of transforming a mandatory task, which is currently still often externally prescribed, into an intrinsically motivated and valued discipline."

  1. Data Steward Track: How to write a Data Management Plan?

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."

  1. Data Scientist Track: Evaluating ML/AI algorithms

Prof. Dr. Marvin N. Wright

Marvin, Computer Engineer and Biostatistician, is the head of an Emmy Noether research group on interpretable machine learning, funded by the German Research Foundation, at the Leibniz Institute for Prevention Research and Epidemiology – BIPS in Bremen, Germany. Since February 2021, he is also Professor of Machine Learning in Statistics at the University of Bremen. He has a research focus on statistical learning and interpretable machine learning and is interested in epidemiological applications to high-dimensional genetic data and longitudinal register data. Marvin is author of several machine learning R packages, e.g. for random forests and neural networks. He taught several machine learning courses at international conferences and at international universities.

"Understanding the general principles of machine learning is the key to successfully apply it in practice. That’s why I want to teach machine learning beyond buzzword bingo."

  1. Data Scientist Track: Machine learning