The program is associated with the German National Research Data Infrastructure (NFDI). NFDI consortia represented in Bremen (NFDI4Health, NFDI4Biodiversity, KonsortSWD, NFDI4Ing, NFDI4DataScience, NFDI4Earth, NFDI-MatWerk, NFDI4Microbiota) participate in the development and operation of the training courses and the Data Train concept and curriculum is shared with the consortia. In 2022, the curriculum will be  aligned with competence frameworks of national and international Research Data Management and Data Science training programs and a handbook will be developed to enable the roll-out of the program concept at additional locations.

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

Data Train is being established in close collaboration with the Data Science Center at the University of Bremen. In addition to joint development work, the Data Science Center established in 2020 provides the Data Science Center provides the technical infrastructure for the operational implementation of courses.

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.

Dr. Daniel Otero Baguer

 

Daniel Otero Baguer is a Post-doctoral Researcher and Head of Digital Pathology Group at the Faculty Mathematics and Computer Science of the University of Bremen.

In his research he deals with inverse problems, machine learning, image and signal processing, computational engineering, parameter indentification and computational pathology.

 

 

Data Train courses

Operator Track - Data Scientist:  Deep learning

Contact

otero(at)math.uni-bremen.de

ORCID, More

"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." – Daniel Otero Baguer

    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.

    His research focuses 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. In this context, it is task of the law to resolve this conflict.

     

    Data Train courses

    Starter Track:  Data protection and licenses

    Contact

    bbuchner(at)uni-bremen.de

    ORCID, More

    "Research Data Management is an important as well as difficult challenge, which can only be met if all the disciplines concerned closely cooperate." –  Benedikt Buchner

      Dr. Arjun Chennu

       

      Arjun Chennu is Group Leader of the team Data Science and Technology at the Leibniz Centre for Tropical Marine Research (ZMT) in Bremen.

      His research explores ways to leverage the diverse and rapidly growing techniques in data science, machine learning and statistical modelling towards data-driven analytics for the benefit of tropical marine sciences.

       

       

      His 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 Train courses

      Operator Track – Data Steward: Reproducibility in science: How and why?

      Contact

      arjun.chennu(at)leibniz-zmt.de

      ORCID, More

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

        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, he 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.

        Data Train courses

        Operator Track – Data Scientist Track: Quantitative analyses for data science

        Contact

        dickhaus(at)uni-bremen.de

        ORCID, More

        "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." –  Thorsten Dickhaus

          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.

          Her 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.

          Data Train courses

          Starter Track: Asking the right research questions in data science

          Operator Track – Data Scientist Track: Causal learning

          Contact

          didelez(at)leibniz-bips.de

          ORCID, More

          "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." –  Vanessa Didelez

            Dr. Martin Dörenkämper

             

            Martin Dörenkämper is a Post-Doctoral Researcher at the Fraunhofer institute for Wind Energy Systems (IWES) in the Department of Aerodynamics, CFD and Stochastic Dynamic in 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.

            Data Train courses

            Starter Track: Managing confidential data

            Contact

            martin.doerenkaemper(at)iwes.fraunhofer.de

            ORCID, More

            "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." –  Martin Dörenkämper

              Prof. Dr. Rolf Drechsler

              Foto von Prof. Dr. Rolf Drechsler
              © Universität Bremen / AGRA

              Rolf Drechsler received the Diploma 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).

              Data Train courses

              Starter Track: Computer science basics for data science

              Contact

              drechsler@uni-bremen.de

              ORCID, More

               

                PD Dr. Christian Fieberg

                 

                Christian Fieberg is Post-Doctoral 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 is part of his research.

                Data Train courses

                Operator Track – Data Steward: Getting started in R

                Operator Track – Data Steward: Erste Schritte mit MATLAB

                Contact

                cfieberg@uni-bremen.de

                More

                "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." – Christian Fieberg

                  Prof. Dr. Frank Oliver Glöckner

                  © Alfred-Wegener-Institut

                  Frank Oliver Glöckner is Professor of 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.

                  Data Train courses

                  Starter Track: Data and information management

                  Operator Track – Data Steward Track: How to write a data management plan?

                  Contact

                  frank.oliver.gloeckner(at)awi.de

                  ORCID, More

                  "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." – Frank Oliver Glöckner