Is it advisable to combine two dataset? I have two datasets on heart rate of subjects that were recorded in two different places (two different continent to be exact). The two research experiments aimed to find the subjects' emotions based on how much their heart rate change over time. I am using machine learning to predict the subjects' emotions and i am getting acceptable result when tested separately on each dataset. However, i get even better result if i merge the two datasets. 

I am not however sure if combining the two datasets is acceptable. As I am combining two somehow different datasets, will it create statistical bias? How should i report my finding in a journal paper?
Adding to what @Super_John said, if adding continents as a Feature, then you can also probably have at-least 2 more features as well,


The Latitude
The Longitude


Also add another temporary column to indicate the Source (like $1$ to $1st$ df, $2$ to $2nd$ df etc), So that we can add Colors to the k-means

So now we can have a k-means Cluster to see whether values are overlapping or not...
(Trying to see it in an Unsupervised Way)

(The analogy is equivalent to the  fact that that you can cluster time(24  hours in a day) in a cyclic fashion , like plotting  $sin(x)$, $cos(X)$ and then trying to cluster them)

Have a look at this answer,
Features Selection, Extraction