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KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association Steinbuch Centre for Computing (SCC) www.kit.edu.

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Presentation on theme: "KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association Steinbuch Centre for Computing (SCC) www.kit.edu."— Presentation transcript:

1 KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association Steinbuch Centre for Computing (SCC) www.kit.edu A Summary of the Interim Meeting of the RDA IG Education and Training for Research Data Handling (January 14 th and 15 th, 2016, Oxford) subjective and incomprehensive Christopher Jung, KIT

2 Steinbuch Centre for Computing 2 Laura Molloy Yuri Demchenko Patrice Ajai-Ajagbe Simon Hodson Andrea Manieri Steve Brewer Veerle Van Den Eynden Stéphane Goldstein Hugh Shanahan Thanks for the very interesting and productive discussions! Full notes available at: https://docs.google.com/document/d/1Y2c4NDKHq_euBmSnN0Ui94vNc9pH_TXQavMRH0XAhPE/pub 25.2.2016 Christopher Jung Participants in the discussions

3 Steinbuch Centre for Computing 3 General Challenges Data scientist as an individual is almost a fiction There will be a family of professions (e.g. researchers, research managers, librarians, curators,...) involved in the research data life cycle How to include all research disciplines? (-> language challenges) Different levels: awareness, knowledge and skills Training Needs to address all general challenges Multiplier effect is important (training the trainers) Competence Frameworks Goal of EDISON (aligned with European e-competence framework 3.0) Competences need to be recognized by industry (employability) 25.2.2016 Christopher Jung General discussion

4 Steinbuch Centre for Computing 4 „Technical“ skills: Computational thinking (statistics software, programming) Design and planning of data (tools, equipment, formats) Data cleaning, data analysis, modelling, statistics Interpretation, visualization, storytelling (incl. describing impact) Data sharing/publishing (in particular enabling data reuse) „Non-technical“ skills: Communication for productive interaction of people, roles and institutions (in particular interdisciplinary interaction) Understanding of and adaptation to new policies (e.g. Open Data) Risk management Fostering „data careers“ (in academia and industry) 25.2.2016 Christopher Jung Discussion of skills


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