Empirical data have a troublesome way of being both factually true, yet difficult to understand because they exist within a complex web of even more data. Worse, forecasting what the future will look like is an even greater challenge. Scientists have, for a long time, been working on these problems, often with great success, but the venue for reporting is generally peer reviewed journals. In 2014/15, we are now at the point that policy makers must have faster access, and in a context-appropriate way, to utilize the forecasts made by scientists. At CFS, there has been an effort to build integrated systems of communities, of data, and of models. What is still lacking is a delivery mechanism to put those forecasts into the hands of the policy makers.
SpaDES simulations typically start and end at a pre-specified times.
So how do you end a simulation based on some other stopping criteria, such as when some variable reaches a particular value?
Rmarkdown with Rstudio and for all stages of my scientific projects has been a remarkable shift in how my work gets done!
There are so many advantages to this type of workflow, not least of which are reproducibility and transparency (both are crucial for scientists as well as public servants).
I’ve been using this approach as much as possible recently, and I’m quite happy with it.
The entire process can be done using
Rmarkdown etc. but there are still a few challenges which I’ll touch on below.