  Developers in data science and other domains frequently use computational
notebooks to create exploratory analyses and prototype models. However, they
often struggle to incorporate existing software engineering tooling into these
notebook-based workflows, leading to fragile development processes. We
introduce Assembl\'{e}, a new development environment for collaborative data
science projects, in which promising code fragments of data science pipelines
can be contributed as pull requests to an upstream repository entirely from
within JupyterLab, abstracting away low-level version control tool usage. We
describe the design and implementation of Assembl\'{e} and report on a user
study of 23 data scientists.
