gh it with 100 normalized queries to get a count, and there are 4852 features. Lots of them are probably useless, but choosing which ones is probably half the battle. These are ~230MB in pickle format, which stores the floats in binary. This can then be compressed to ~20MB with gzip, so the data size isn't particularly insane. In a released dataset i would probably use 10k normalized queries, meaning about 100x this size Could plausibly release as csv's instead of pickled numpy arrays. That will probably increase the data size further,