Dataset Information

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

ABSTRACT:

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.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Anton Feenstra K

PROVIDER: S-EPMC6022589 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Anton Feenstra K K Abeln Sanne S Westerhuis Johan A JA Brancos Dos Santos Filipe F Molenaar Douwe D Teusink Bas B Hoefsloot Huub C J HCJ Heringa Jaap J

Bioinformatics (Oxford, England) 20180701 13

<h4>Motivation</h4>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.<h4>Results</h4>Here we present our experiences with shaping and run ...[more]

PMID: 29950011

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Project description:BACKGROUND:Every year, the life science field spends billions of dollars on educational activities worldwide. The continuing professional development of employees, especially in this field, encompasses great challenges. Emerging technologies appear to offer opportunity, but relatively little research has been done on the effectiveness of pedagogies and tools that have been used in the life sciences, and even less research has been devoted to understanding the potential power of emerging options that might determine the field's future. OBJECTIVE:In collaboration with the Life Sciences Trainers & Educators Network (LTEN), this study investigated the current state of the pedagogies and tools currently adopted by corporate training professionals in the life sciences as well as the professionals' perceptions of the impacts of emerging technologies on training. METHODS:This study adopted a mixed methods approach that included a survey and a follow-up interview. The survey consists of 18 broad questions with 15 subquestions in each of the five specific sectors of the life sciences field. Interviews were conducted by phone and lasted approximately 40 minutes, covering 18 questions designed to follow-up on findings from the survey items. RESULTS:Both survey and interview results indicated that the professionals were not satisfied with the status quo and that training and education in this field need to change. Most of the techniques and tools currently used have been used for some time. The professionals surveyed were not satisfied with the current techniques and tools and did not find them cost-effective. In addition, the respondents pictured the future of training in this field to be more engaging and effective. CONCLUSIONS:This is the first study in a series designed to better understand education and training in the life sciences on a macro level, in order to build a foundation for progress and evolution of the future landscape. Next steps involve developing strategies for how to extend this vision throughout individual organizations.

Dataset Information

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

Motivation

Results

Supplementary information

Publications

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

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