 Suppose you have three data sets and they represent changes in some genome. So in the first data set, these are your positions in the genome, Euclidotide 10, 12, 20, and so on. And this is the type of change that you have in this sample. So in sample one, you have these changes. In sample two, you have something like that, and in sample three, you have those. And suppose you want to merge all of this into a single table and use position as a kind of a common field, a hinge, if you wish to hook all the data on. Let's see how this works. First of all, I am going to create a list out of these data sets. So I'm just going to select them all and for all selected, build data set list. I will call this list samples, for example, and I will hide original elements. So here is my list. Again, if we look, we can see that obviously the list has data of the same type, but of course there are differences. So all data has the same structure, it has position column, and it has change column, but it has different content. So in some samples, site 20 is changed. In some samples, it's absent and so on. So what I want to do, I want to join all of them on that position column. And for that, I'm going to use column join tool. This is a very powerful tool. And in this case, we have collection with just three data sets, but it will as easily work on a collection with 10,000 data sets. So let's select the collection question. And the identifier column, in other words, the column on which to join things, in our case, is the first column. You will also notice that our file, our data set has header. So this is the header line. And so we're just going to tell the tool that it has one, each input file has one header line. And let's add the column name to the header. And this will be our empty character. We'll see what that means. And let's run it. In the end, we're going to get just one data set and let's see how it looks like. So you can see that we have three positions because this is the union of positions in all three samples. And for each data set, we have changes. So this is a changes in sample one, changes in sample two, changes in sample three. And you can see that empty data sets, there is nothing happening in position 12 in sample three, for example, they are represented by dots.