Shawn Wallace, Applications Engineer, EDAX
We all give presentations. We write and review papers. Either way, we have to be critical of our data and how it is presented to others, both numerically and graphically.
With that said, I thought it would be nice to start this year with a couple of quick tips or notes that can help with mistakes I see frequently.
The most common thing I see is poorly documented cleanup routines and partitioning. Between the initial collection and final presentation of the data, a lot of things are done to that data. It needs to be clear what was done so that one can interpret it correctly (or other people can reproduce it). Cleanup routines can change the data in ways that can either be subtle (or not so subtle), but more importantly they could wrongly change your conclusions. The easiest routine to see this on is the grain dilation routine. This routine can turn noisy data into a textured dataset pretty fast (fig. 1).
Luckily for us, OIM Analysis
The other common issue is not including the full information needed to interpret a map. I really need to look at 3 things to get the full picture for an EBSD dataset: the IPF map (fig. 3), the Phase Map (fig. 4) and the IPF Legend (fig. 5) of those phases. This is very important because while the colors used are the same, the orientations differ between the different crystal symmetries.
Below is a multiple phase sample with many crystal symmetries. All use Red-Green-Blue as the general color scheme. By just looking at the general IPF map (fig. 3), I can easily get the wrong impression. Without the phase map, I do not know which legend I should be using to understand the orientation of each phase. Without the crystal symmetry specific legend, I do not know how the colors change over the orientation space. I really need all these legends/maps to truly understand what I am looking at. One missing brick and the tower crumbles.
Being aware of these two simple ideas alone can help you to better present your data to any audience. The fewer the questions about how you got the data, the more time you will have to answer more meaningful questions about what the data actually means!