With applied data sciences, especially machine learning (ML) and AI, gaining their renewed momentum in Chemistry, the interests in learning basic programming skills have also been growing significantly in the past few years. Even those experimentation-focused labs are starting their ML and AI exploration to improve their research efficiency or to implement automation. However, most undergraduate and graduate curricula in chemistry are still focusing more on training experimentalists. There is a gap for graduate students and junior scientists to learn those basic research computing skills. To meet such needs on MIT campus, we introduced Carpentries (https://carpentries.org/) workshops on Unix/Shell, Python, R, and Git/GitHub and started building a local learning community. Since 2019, we have hosted five rounds of the workshops for over 150 learners and chemists have been the largest group of participants for these workshops. In this presentation, we will introduce the strategies on how to build an instructor and helper pool on campus, how to integrate data management as well as open and reproducible research skills into the curriculum, and how to leverage the Carpentries’ global learning community to engage local learners. We will also discuss our plan to customize the Carpentries curriculum for chemists and form a sub-learning community for chemists.

Building local carpentries learning community: Engaging chemists to learn data skills and reproducible research

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.