Gittin' after it...

Well, it's time. I've finally resolved to ride the new wave of science communication and up my online presence. I started out by updating my long-overlooked ResearchGate profile, and by learning a new skill:


Github!


For a few days, this mysterious jargon was the bane of my existence, and then finally on Tuesday it all clicked. The ultimate goal is to make publicly available the complete R code for each chapter of my dissertation with an accompanying readme document and Rmarkdown tutorial. If I had all the time and money in the world I would also write an R package for my marsh accretion model, but do I actually have weeks to blast my eyeballs out staring at DIY coding instructions? Only time will tell, folks.


For now, Github will have to do. There is the unfortunate caveat that I'm not allowed to share my raw data (since they belong to the Feds), so I'll have to post a fake dataset for each set of code. In any case, this should satisfy the new call for "data transparency" in the science community, and in many ways it will inspire me to hold myself accountable for the quality of my work. There's no cutting corners if anyone in a dark room can scrutinize your brainchild.


For practice purposes, I started with my Inundation model, which is probably the simplest possible example. Basically, the code takes water level data from a continuous data logger, and then calculates out the range of inundation durations from low tide to high tide. All you need is the raw data, mean tidal level (MTL) of the system, and logger sensor elevation.


The data follow a generalized logistic decay function like this:

Overall, model fit is pretty good, but it always overestimates the lower values.


In any case, I hope at least somebody takes a look. Because what's the point of having a Github if nobody is there to admire your code?