Ariel Deardorff
Assessing the impact of introductory programming workshops on the computational reproducibility of biomedical workflows
San Francisco Public Library|University of California, San Francisco
introduction biomedical research becomes dataintensive computational reproducibility growing area importance unfortunately many biomedical researchers received formal computational training often struggle produce results reproduced using data code methods programming workshops tool teach new computational methods always clear whether researchers able use new skills make work computationally reproducible methods mixed methods study consisted indepth interviews 14 biomedical researchers participation introductory programming workshop interviews participants described research workflows responded quantitative checklist measuring reproducible behaviors interview data analyzed using thematic analysis approach pre post workshop checklist scores compared assess impact workshop computational reproducibility researchers workflows results pre post scores checklist reproducible behaviors change statistically significant manner qualitative interviews revealed several participants made small changes workflows including switching open source programming languages data cleaning analysis visualization overall many participants indicated higher levels programming literacy interest training factors enabled change included supportive environments immediate research need barriers included collaborators resistant new tools lack time conclusion none workshop participants completely changed workflows many incorporate new practices tools methods helped make work reproducible transparent researchers indicates programming workshops offered libraries organizations contribute computational reproducibility training researchers