Posted: October 17, 2017

Open Source Training in Computational Competence and Hands-on Data Analysis


Rigor, transparency, and reproducibility, as well as the curation and analysis of data, are integral to research design and implementation. Moreover, these are important components of biosciences graduate research training. As part of a one-year supplement to enhance rigor and reproducibility in research, we implemented a set of career development activities for doctoral trainees in Cellular and Molecular Biology at Stanford University to enhance their training in data analysis and computational competence. These activities included: 1) partnering with non-profit educational organizations, Software and Data Carpentry, to host three workshops and one instructor training, 2) developing and teaching a course in data image analysis, created in the Carpentry format, and 3) creating tools to assess the impact of these events and programs. Software Carpentry and Data Carpentry teach researchers fundamental skills in “data organization, management, and analysis to increase data literacy and improve research efficiency.” Having Carpentry-trained instructors on campus and building upon cross-campus partnerships will enable us to maintain these efforts long-term. Our goal is to develop a self-sustaining Data/Software Carpentry community at Stanford so that these workshops can be offered on a continuing basis to provide ‘just in time’ training to trainees at multiple stages of their training. Preliminary findings indicate that this training improves trainee confidence in data analysis practices and dissemination, which are anticipated to promote increased rigor and reproducibility.


Research, Reproducibility, Computational, Carpentry Workshop, Rigor


Martha S. Cyert, PhD, Stanford University, Department of Biology

Latishya Steele, PhD, Stanford Medicine, Office of Graduate Education

Amy E. Hodge, PhD, Stanford University Libraries

Miriam Goodman, PhD, Stanford University, Department of Molecular and Cellular Physiology