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The MLSN guidelines, data, and reproducible research

Our preprint on the MLSN guidelines is now available. It was published today at PeerJ Preprints, as Minimum soil nutrient guidelines for turfgrass developed from Mehlich 3 soil test results. We wanted to share what we have done so far, make this paper available for citation in case anyone needs to cite something more technical than our 2014 GCM paper, and also solicit feedback about this paper before we submit it for peer review.

If you are interested in this, you probably care just about the article. Maybe just the abstract of the article. Maybe the abstract and a glance at the introduction and then a skip to the discussion and conclusions. That's fine. We'll be glad if you read any of it.

Beyond the article itself, I want to share what I'm most interested in with this project. That's the reproducibility of it. And the openness of it. We are sharing the results, and also the data and the code to generate the results and the code to generate the paper itself.

Mlsn_github_pages

 

We  want to make sure that anyone who wants to read it can do so, so we share it as a preprint, and will make sure if a later version is published, that it is open access. You won't have to worry about clicking to read the article and hitting a paywall. I hit a couple paywalls this afternoon in my own research, and snapped these screenshots.

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Selection_066

Those type of paywalls won't happen with this project.

Beyond that, however, the paper is reproducible*. That is, we are sharing all the data, all the code, and all the text; you can run the files and generate the exact same results -- in fact, the exact same pdf. You probably don't have all the software on your computer to do that, but you could. It is all open source and free. R, LaTeX, and some R libraries. We used knitr, VGAM, xtable, and dplyr in this project. You can check our files and see which libraries we used. You can check the code to see how we made the figures. How the values in the tables were calculated. You can see what functions we wrote to calculate the MLSN guidelines.

With this type of work, you can see what we did, and you can also see how we did it.

Furthermore, we've made the data, as we did with the Global Soil Survey data, freely available with no copyright. You want to study soil test results and have a need for more than 16,000 samples, or a subset of them? Have at it!


*reproducible research -- if you are interested in this, I suggest reading this post at Simply Statistics:

The Real Reason Reproducible Research is Important

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