We think this is an interesting paper for anyone who wants to learn to build an API with the R package plumber. This is a novel method in health economics, but we believe will help improve the transparency of modelling methods in our field.
Metadata annotation is key to reproducibility in sequencing experiments. Reproducing this research using the scripts provided will also show the current level of annotation in years since 2015 when the paper was published.
- This paper is a good example of a standard social science study that is (I hope!) fully reproducible, from main analysis, to supplementary analyses and figures. - I have not yet received any external feedback w.r.t. its reproducibility, so would be interested to see if I have overlooked any gaps in the reproduction workflow that I anticipated.
If all went right, the analysis should be fully reproducible without the need to make any adjustments. The paper aims to find optimal locations for new parkruns, but we were not 100% sure how 'optimal' should be defined. We provide a few examples, but the code was meant to be flexible enough to allow potential decision makers to specify their own, alternative objectives. The spatial data set is also quite interesting and fun to play around with. Cave: The full analysis takes a while to run (~30+ min) and might require >= 8gb ram.
The results of the individual studies (4) could be interpreted in support for the hypothesis, but the meta-analysis suggested that implicit identification was not a useful predictor overall. This conclusion is an important goalpost for future work.
I guess it could be a cool learning experience. The paper is written with knitr, uses a seed, is part of the R package it describes, was openly written using version control (SVN, R-Forge) and is available in an open access journal (@up_jors).
The focus of the project is reproducibility. Here we show the differences to access data compared to similar initiatives: https://ropensci.org/blog/2019/05/09/tradestatistics/. Also, similar projects have obscure parts, while our exposes the code from raw data downloading to dashboard creation.