I have a few ideas.
First, the best place to start is with data from actual tournaments. The best place to get that data is from online sites. If the data to be collected were clearly defined, one might be willing to cooperate. Poker Room, for example, has posted the limit EV data that you and I have debated before. Since the data that would be collected would not reveal any site-specific or player-specific information, one or more sites might just be willing to help out.
Second, even if no online site were willing to assist, it might be possible to collect the data from a group of players, especially if the data set required for any meaningful analysis would not be insurmountable.
Third, if online sites were not cooperating and sufficient data were not available from players, I would give some thought to taking data from bot v. bot simulations. There are a number of increasingly sophisticated poker bots available on the market. While I would have concerns about the validity of applying bot data to human play, it might be illuminating in this context. For example, if the bot data were consistent with a null hypothesis of a linear equity function, my faith in that assumption would be different than if the data suggested a non-linear relationship.
Fourth, aside from collecting tourney or bot data, there are other ways to attack the problem. For example,
online electronic markets have been used to predict presidential elections and economic indicators. One way of approaching the equity function would be to ask, how much money would a third-party be willing to pay for a stack size of
x in a HU freezeout? There are ways, such as online markets, to determine that answer without having to collect and analyze a gazillion data points.
The Shadow (who appreciates your patience while I fixed the links)