Releasing the code is the very least you should do to make your analysis reproducible. I would be surprised if it was possible to exactly reproduce the results from the paper alone.
> Documenting implementation details without making data, models and code publicly available and usable by other scientists does little to help future scientists attempting the same analyses and less to uncover biases. Authors can only report on biases they already know about, and without the data, models and code, other scientists will be unable to discover issues post hoc.
Even better would be to containerize all software dependencies and orchestrate the analysis with a workflow manager. The authors of the above paper refer to that as "gold standard reproducibility"
From Heil et al. (https://www.nature.com/articles/s41592-021-01256-7):
> Documenting implementation details without making data, models and code publicly available and usable by other scientists does little to help future scientists attempting the same analyses and less to uncover biases. Authors can only report on biases they already know about, and without the data, models and code, other scientists will be unable to discover issues post hoc.
Even better would be to containerize all software dependencies and orchestrate the analysis with a workflow manager. The authors of the above paper refer to that as "gold standard reproducibility"