Question: I am looking for some relatively simple data sets for testing and comparing different training methods for artificial neural networks. I would like data that won't take too much pre-processing to turn it into my input format of a list of inputs and outputs (normalized to 0-1). Any links appreciated.

Answer: 
Why not try something simple like the sin function as the training data? Since you are comparing the training methods and don't really care about what you are training the network for, it should work and be easy to generate the training data.

Train the network using sin(x) where x is the input and the output is the value of the function. An added benefit in your case is that the absolute value of the result is already in the range 0-1. It would equally work with other mathematical functions.
I learnt ANNs as an undergraduate by using them to perform OCR (Optical Character Recognition). I think this is a nice use case.

Scan in two pages of text, extract the letters and form training/testing datasets (e.g. 8x8 pixels leads to 64 input nodes), label the data. Train the ANN and get a score using the testing dataset. Change the network topology/parameters and tune the network to get the best score.