Training for translation between disciplines: A philosophy for life and data sciences curricula

K. Anton Feenstra*, Sanne Abeln, Johan A. Westerhuis, Filipe Brancos Dos Santos, Douwe Molenaar, Bas Teusink, Huub C.J. Hoefsloot, Jaap Heringa

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Motivation: Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. Results: Here we present our experiences with shaping and running a masters? programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula.

Original languageEnglish
Pages (from-to)i4-i12
Number of pages9
Issue number13
Early online date27 Jun 2018
Publication statusPublished - 1 Jul 2018


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