About Data Train

Data Train—the cross-disciplinary graduate program for Research Data Management (RDM) and Data Science (DS) of the U Bremen Research Alliance (UBRA)—enhances competencies in RDM and DS. We empower early career researchers in data literacy for a data-heavy future.

Centralized Training in Research Data Management and Data Science

Data Train is dedicated to enhancing competencies in Research Data Management (RDM) and Data Science (DS) for doctoral researchers across various institutes. As data-driven science becomes crucial in addressing today's research challenges, the demand for skilled individuals in these fields grows. Our program aims to fill this gap by providing comprehensive training that strengthens basic competencies in data literacy, RDM, and DS. The program and its development is associated with the German National Research Data Infrastructure (NFDI) initiative and accompanied by the Research Data Working Group

Figure showing competences of Data Stewards and Data Scientists.
Competences of Data Stewards and Data Scientists.   © Hörner et al. 2021; https://doi.org/10.17192/bfdm.2021.3.8343.

 

 

The Data Train model

Our Curriculum consists of a Starter Track with introductory lectures, the Operator Track ‘Data Steward' which equips participants essential skills for effective data management in line with the FAIR Data Principles, and the Operator Track 'Data Scientist' which focuses on extracting insights and knowledge from data through computational analysis.

Furthermore, we offer Data Stories—inspiring stories about data handling, management and science—and monthly Hacky Hour coworking and peer-learning sessions.

Find a more in-detail description of our Curriculum here.

  •  Participation is free of charge. Attendance is confirmed by Proof of Participation.
  •  Interested in learning more about the Data Train program? Read more about the concept, the development of the curriculum and addressed competences in our whitepaper and other publications.
Schematic illustration of the curriculum’s concept. Boxes symbolize individual courses. All courses that belong to one track are connected by a black line. It indicates the thematic structure of each track. Overview lectures of the Starter Track (Level 1) are teal, hands-on workshops of the Operator Tracks (Level 2) are light blue and dark blue. Light red boxes stand for discipline-specific courses that could be offered in the scientific domains or the NFDI consortia and can build on the basics taught in Data Train program. © Hörner et al. 2021; https://doi.org/10.17192/bfdm.2021.3.8343
Schematic illustration of the curriculum’s concept. Boxes symbolize individual courses. All courses that belong to one track are connected by a black line. It indicates the thematic structure of each track. Overview lectures of the Starter Track (Level 1) are teal, hands-on workshops of the Operator Tracks (Level 2) are light blue and dark blue. Light red boxes stand for discipline-specific courses that could be offered in the scientific domains or the NFDI consortia and can build on the basics taught in Data Train program. © Hörner et al. 2021; https://doi.org/10.17192/bfdm.2021.3.8343
To the courses
Curriculum

Cooperating in Data Train

Universität Bremen
Max Planck Institute for Marine Microbiology
Leibniz Institute for Material-Oriented Technologies
Leibniz Institute for Prevention Research and Epidemiology
Leibniz Centre for Tropical Marine Research
Fraunhofer Institute for Medical Image Computing
Fraunhofer Institute for Wind Energy Systems and Energy System Technology
Fraunhofer Institute for Manufacturing Technology and Advanced Materials
German Maritime Museum
German Aerospace Center – Institute of Space Systems
In­sti­tute for the Pro­tec­tion of Mar­itime In­fras­truc­tures
German Research Center for Artificial Intelligence
Alfred Wegener Institute
DLR Institute for Satellite Geodesy and Inertial Sensing
DLR IMF

Data Train is supported by the Federal Ministry of Research, Technology and Space and funded by the European Union - NextGenerationEU.

 
 
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