Artifact for "Automatic Data Enumeration for Fast Collections", accepted at CGO'26 Abstract: Data collections provide a powerful abstraction to organize data, simplifying development and maintenance. Choosing an implementation for each collection is a critical decision, with performance, memory and energy tradeoffs that need to be balanced for each use case. Specialized implementations offer significant benefits over their general-purpose counterparts, but also require certain properties of the data they store, such as uniqueness or ordering. To employ them, developers must either possess domain knowledge or transform their data to exhibit the desired property, which is a tedious, manual process. One such transformation---commonly used in data mining and program analysis---is data enumeration, where data items are assigned unique identifiers to enable fast equality checks and compact memory layout. In this paper, we present an automated approach to data enumeration, eliminating the need for manual developer effort. Our implementation in the MEMOIR compiler achieves speedups of 2.16x on average (up to 8.72x) and reduces peak memory consumption by 5.6% on average (up to 50.7%). This work shows that automated techniques can manufacture data properties to unlock specialized collection implementations, pushing the envelope of collection-oriented optimization. Artifact Description: Our artifact includes source files for the MEMOIR compiler, with the transformation and extensions described in the paper. In addition to this, it includes the benchmark suite used for evaluation, and plotting scripts. This artifact reports its results by recreating Figures 4, 5, 6, 8a and 9a of the paper. The user is able to configure which experiments they would like to evaluate, this customization is detailed in the artifact appendix. The artifact has been tested on both Intel-x64, AMD-x64, and AArch64. The artifact requires a network connection to download external dependencies and our benchmark suite.