Sampling and more specifically, sampling frequency is a really important and much misunderstood concept in many fields of research. As we’ll see in this analogy-ridden post, it’s important to understand sampling in both time and space.
In previous posts, we’ve almost taken for granted how Fiji/ImageJ deals with multidimensional data. Whenever you have more than one image, be it a second channel all the way up to massive 5D datasets, you’re dealing with stacks.
This concept is so fundamental, I thought it deserving of it’s own post. Let’s stack ’em up.
Arguably one of the best things about doing Research using microscopes is the awesome power of the Image for getting your point across. Whether it’s for public outreach, presentation at a conference or just showing off your work at group meeting, a picture says… well you know the rest.
But what if you’re looking at a temporal phenomenon? Sure you could always use a montage but there’s nothing like a moving picture to wow your audience and get pulses racing. In this post we’ll look at a few different ways of turning your multidimensional data into movies.
Bring your own popcorn!