Chattopadhyay et al. (2020) mentioned that "as data scientists [...], they encounter unexpected difficulties—pain points—from limitations in affordances and features in the notebooks, which impact their productivity and disrupt their workflow." (pag. 1)

These pain points represent an opportunity to change or challenges in a specific aspect of reproducibility.

References

Chattopadhyay, S. Prasad, I. Henley, A. Sarma, A. Barik, T. (2020). What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities. Oregon State University, Microsoft, University of Tennessee-Knoxville. see link on https://dl.acm.org/doi/pdf/10.1145/3313831.3376729