Alloy is a modeling language that combines relational first-order logic and temporal logic while providing powerful automated analyses via the Alloy Analyzer. Recent efforts in tool development and teaching of Alloy have contributed the Alloy4Fun dataset enabling many analyses of fine-grained model editing histories. We present a smaller, but complementary dataset  \(\textsf {FMP}_\textsf {als}\) of similar editing granularity. While the Alloy4Fun dataset captures users filling in predefined predicates, our dataset is more diverse and users develop all parts of Alloy models including signatures, fields, facts, and commands. We illustrate the differences between the datasets, define a Halstead metric to measure the difficulty of models, and evaluate model edit paths from both datasets on various metrics.