I used a lot of different tools and strategies to make this paper easily reproducible at different levels. There's Docker container for the highest level of reproducibility, and package versions are managed with renv. The data used in the paper is hosted on Zenodo to avoid long queue times when downloading from the Climate Data Store and future-proof for when it goes away and checksumed before using it.
The code and data are both on GitHub. The paper has been published in Wellcome Open Research and has been replicated by multiple other authors.
In the middle of the COVID-19 pandemic, this paper provided important evidence regarding the effect of misinformation on vaccination intent. Its analyses and conclusions were extremely important for decision makers. Therefore, it is also important that the analyses are reproducible.
Even though the approach in the paper focuses on a specific measurement (clumped isotopes) and how to optimize which and how many standards we use, I hope that the problem is general enough that insight can translate to any kind of measurement that relies on machine calibration. I've committed to writing a literate program (plain text interspersed with code chunks) to explain what is going on and to make the simulations one step at a time. I really hope that this is understandable to future collaborators and scientists in my field, but I have not had any code review internally and I also didn't receive any feedback on it from the reviewers. I would love to see if what in my mind represents "reproducible code" is actually reproducible, and to learn what I can improve for future projects!
I suggested a few papers last year. I’m hoping that we’ve improved our reproducibility with this one, this year. We’ve done our best to package it up both in Docker and as an R package. I’d be curious to know what the best way to reproduce it is found to be. Working through vignettes or spinning up a Docker instance. Which is the preferred method?
The paper describes pyKNEEr, a python package for open and reproducible research on femoral knee cartilage using Jupyter notebooks as a user interface. I created this paper with the specific intent to make both the workflows it describes and the paper itself open and reproducible, following guidelines from authorities in the field. Therefore, two things in the paper can be reproduced: 1) workflow results: Table 2 contains links to all the Jupyter notebooks used to calculate the results. Computations are long and might require a server, so if you want to run them locally, I recommend using only 2 or 3 images as inputs for the computations. Also, the paper should be sufficient, but if you need further introductory info, there are a documentation website: https://sbonaretti.github.io/pyKNEEr/ and a "how to" video: https://youtu.be/7WPf5KFtYi8 2) paper graphs: In the captions of figures 1, 4, and 5 you can find links to data repository, code (a Jupyter notebook), and the computational environment (binder) to fully reproduce the graph. These computations can be easily run locally and require a few seconds. All Jupyter notebooks automatically download data from Zenodo and provide dependencies, which should make reproducibility easier.
It uses the drake R package that should make reproducibility of R projects much easier (just run make.R and you're done). However, it does depend on very specific package versions, which are provided by the accompanying docker image.
This paper is reproduced weekly in a docker container on continuous integration, but it is also set up to work via local installs as well. It would be interesting to see if it's reproducible with a human operator who knows nothing of the project or toolchain.