As many of you already know, I’m a big fan of PolyCounter from Takeshi Nakagawa (find it here if you haven’t yet: http://polsystems.rits-palaeo.com/). PolyCounter introduced a modern way to acquiring data of Quaternary-palaeo-ecological samples, replacing the old-fashioned pen and paper by your computer. With PolyCounter, your counting data are instantly accumulated and stored in a numeric file, which is just what you need before you can actually visualise your data, and eventually apply fancy statistics on them.
https://giphy.com/embed/DHqth0hVQoIzS
Some potential users, however, have a (legitimate) fear of loosing data. Well, I would first argue that backing up data is one’s self duty, and actually a most-recommened practise for any kind of numerical data. But, regarding these counting data, I would mostly tell about particular features of PolyCounter.
First, PolyCounter offers to save summary tables. Save and print them, it will look just like the good-old paper counting sheets. Second, and most important, PolyCounter offers to save the entire history of your counting session. And here comes the amazing part: I created a “software” that can read PolyCounter’s histories and produce things like this:
… and even like this:
You got it? The first picture is just like the accumulation curve that PolyCounter shows real-time while counting, plus some nifty additions (such as when each taxa occurred for the first time). The second is the stabilisation of the proportion of each taxa while counting. This is extremely helpful to assess whether the counting effort is sufficient or not. https://giphy.com/embed/12AZjJcRGhu9nG
The thing is, these nice visualisations can be produced any time, provided the related history has been saved. From now on, I’ll be saving each single counting session in a dedicated archive folder (which is backed up as well :D). This will offer me the opportunity—whenever!—to come back to my data and assess their reliability. This will also have me covered in case I loose the actual PolyCounter counting files. And this is why we are entering a new era for the documentation of pollen* data!
My software comes as a R notebook. That’s a text file that uses Markdown/Rmarkdown syntax, that can be easily adapted in Rstudio to fit other people’s personal settings (e.g. file names, sample naming scheme, …). I’m likely to share it, just ask me if you’re interested 😉
* Actually, any kind of data that implies identification and numbering.
