My understanding of why this isn't often the case in the Social Sciences is that data is usually extremely expensive to collect. The researchers usually spend many, many hours writing proposals, revising it, resubmitting it, until they finally have the funding to collect the data their interested in. Then they actually have to get people into a laboratory, or send someone out to do field work (sometimes halfway across the world), or any number of expensive procedures.

And then, after data is collected, they begin analyzing it and seeing what are the interesting patterns in the data. And this takes a lot of time, and often you just want to show one thing at a time. In the current environment of publish-or-perish, and grant providers asking what you've done with their money, you might want to take that data you worked so hard to obtain, and milk quite a few publications out of it. If you openly release the data, then others can easily find interesting patterns in your data before you can, beating you to publication, and diminishing your track record for future grants, tenure, etc.

So your point is extremely valid. But the counter point of why open data isn't feasible is, if a researcher does all the hard legwork necessary to get to the interesting analysis stage, why shouldn't they reap the rewards of their hard work (namely, publications and recognition).

A few things have been proposed: verification projects (one particular other research group gains access to your data and verifies your results, but is not allowed to use the data otherwise), and grace periods (you have to release your data 3 years after a study or something, giving you time to milk out publications, but still allowing for general verification later).

Generally speaking, true scientific verification should also involve the complete recollection of the data. But when data collection is extremely expensive, this is typically infeasible.

[I work in a field that's almost all open access, and research data is almost always available upon request. So I agree with you. But data is typically incredibly cheap in my field, so the problem above doesn't really apply.]