And preparing data for publication is lots of work [1] that unfortunately doesn't 'pay' in today's scientific culture.mit even runs the risk of others 'mining' publications from your data before you have the chance to do so.
The best reply to questions you can expect is "that's a nice follow-up idea. Let's work on it together". Risk there is that the scientists may be working in some other direction that they find more promising.
[1] for example, you have 100 sets of data, but only used 78 in _this_ publication for various reasons (chemical used was applied at incorrect time, for too long, was out of date, subject was too old, had an existing condition, etc). You explain that in the publication in a small paragraph. In your dataset, you probably did it by copying the dataset and removing the ones not suitable for the task at hand. Did you keep that selection after the paper was published? Probably not. Even if you did, sending the selected data set out isn't sufficient. Others might want to see all data, so that they can verify the correctness of your selection.
Source control systems could help here, but as I said, providing data unfortunately career-wise doesn't pay at the moment for scientists.
Call me naive but reproducibility is one of the hallmarks of a good scientific publication. I'm also fairly skeptical of studies where the used questionnaires are not openly available...the list goes on (I'd also argue that closed source software should be avoided because "we plugged our data into the not checkable algorithm X and got result Y which we interpreted according to what the authors of the software say we should interpret it as" isn't great either)