3 ms·

If you are a computer scientist, deliver a permalink hosted Docker image for a paper that runs fully-unattended by default and produces the graphs and data in t

If you are a computer scientist, deliver a permalink hosted Docker image for a paper that runs fully-unattended by default and produces the graphs and data in the paper verbatim. If you cannot do this, you must turn in your nerd badge and report to the cryptobros for reassignment. You win a Medal of Honor nerd badge if the code is a literate programming quine embedded in TeX source and plays a crack demoscene track while doing an AI DOOM speedrun in ANSI.

Helmut10001 3y ago
I did exactly that for a recent paper [1]. 1. 9 Jupyter Notebooks attached (with HTML converts to the journal), all figures and statistics generated in notebooks, all commits of the entire 5 year research process versioned in a Gitlab repo, using Jupytext for clarity 2. All base data shared, using HyperLogLog to reduce privacy conflicts 3. Versioned docker image added to our registry, which includes Jupyter and the analysis environment used for the study (Carto-Lab Docker Version 0.9.0 [2]) 4. Post acceptance, I published another notebook how other users can load and work with the shared data, including making (limited) additional inference [3] 5. For the peer review process, I added all (redacted) notebook HTML files to a Github repo [4] It was a fun experiment where I tried a maximum of transparency in research. This maybe added 1 full year of additional work, but I still don't regret it. Given the quite specific audience for this paper, I doubt that anyone has ever tried to open the Jupyter Notebooks - I even doubt that reviewers had a look at them, at least by judging from the comments during peer review. [1] https://doi.org/10.1371/journal.pone.0280423 https://doi.org/10.1371/journal.pone.0280423 [2] https://gitlab.vgiscience.de/lbsn/tools/jupyterlab https://gitlab.vgiscience.de/lbsn/tools/jupyterlab [3] https://kartographie.geo.tu-dresden.de/ad/sunsetsunrise-demo/html/intro.html https://kartographie.geo.tu-dresden.de/ad/sunsetsunrise-demo... [4] https://anonymous-peer12345.github.io/ https://anonymous-peer12345.github.io/
- sacnoradhq 3y ago
  Outstanding! :D Effortless reproducibly plus open source must be the gold standard, regardless of the form it takes. As an aside, back when I used to live in a van, I wanted an app that could hyperlocally find a parkable location on a street or parking lot with the most shade for a given time of year. It seems roughly estimatable if high resolution height information were available and combined with the the sun path. It could also be useful if one wanted to locate housing with minimum insolation.
  - Helmut10001 3y ago
    Just another way landscape preference can be expressed!
anthk 3y ago
Uh, Emacs' literate programming with org-mode and Guix instead of docker grants a 100% reproducibility on your tasks.

Helmut10001 3y ago

I did exactly that for a recent paper [1]. 1. 9 Jupyter Notebooks attached (with HTML converts to the journal), all figures and statistics generated in notebooks, all commits of the entire 5 year research process versioned in a Gitlab repo, using Jupytext for clarity 2. All base data shared, using HyperLogLog to reduce privacy conflicts 3. Versioned docker image added to our registry, which includes Jupyter and the analysis environment used for the study (Carto-Lab Docker Version 0.9.0 [2]) 4. Post acceptance, I published another notebook how other users can load and work with the shared data, including making (limited) additional inference [3] 5. For the peer review process, I added all (redacted) notebook HTML files to a Github repo [4] It was a fun experiment where I tried a maximum of transparency in research. This maybe added 1 full year of additional work, but I still don't regret it. Given the quite specific audience for this paper, I doubt that anyone has ever tried to open the Jupyter Notebooks - I even doubt that reviewers had a look at them, at least by judging from the comments during peer review. [1] https://doi.org/10.1371/journal.pone.0280423 https://doi.org/10.1371/journal.pone.0280423 [2] https://gitlab.vgiscience.de/lbsn/tools/jupyterlab https://gitlab.vgiscience.de/lbsn/tools/jupyterlab [3] https://kartographie.geo.tu-dresden.de/ad/sunsetsunrise-demo/html/intro.html https://kartographie.geo.tu-dresden.de/ad/sunsetsunrise-demo... [4] https://anonymous-peer12345.github.io/ https://anonymous-peer12345.github.io/

sacnoradhq 3y ago
Outstanding! :D Effortless reproducibly plus open source must be the gold standard, regardless of the form it takes. As an aside, back when I used to live in a van, I wanted an app that could hyperlocally find a parkable location on a street or parking lot with the most shade for a given time of year. It seems roughly estimatable if high resolution height information were available and combined with the the sun path. It could also be useful if one wanted to locate housing with minimum insolation.
- Helmut10001 3y ago
  Just another way landscape preference can be expressed!

anthk 3y ago

Uh, Emacs' literate programming with org-mode and Guix instead of docker grants a 100% reproducibility on your tasks.