The announcements and About page indicate an emphasis on visuals and presentation, which I apprI've. But when I think of "modern machine learning," I think of open-source and reproducibility (e.g. Jupyter notebooks).
Will the papers published on Distill maintain transparency of the statistical process?
I see in the submission notes that articles are required to be a public GitHub repo, which is a positive indicator. Although the actual code itself does not seem to be a requirement.
I totally agree that this is very important. While it isn't currently our primary focus, having a publishing platform that can accommodate a variety of content types (including code and data) feels like a step in the right direction.
Comments
The announcements and About page indicate an emphasis on visuals and presentation, which I apprI've. But when I think of "modern machine learning," I think of open-source and reproducibility (e.g. Jupyter notebooks).
Will the papers published on Distill maintain transparency of the statistical process?
I see in the submission notes that articles are required to be a public GitHub repo, which is a positive indicator. Although the actual code itself does not seem to be a requirement.
I totally agree that this is very important. While it isn't currently our primary focus, having a publishing platform that can accommodate a variety of content types (including code and data) feels like a step in the right direction.