Though featurization is important, the datasets used to make conclusions are just as important, if not more so. Information Security researchers often cannot release data, resulting in lack of benchmark datasets and causing cross-dataset generalization to be understudied in this domain. Despite this fact, presence of dataset bias (especially negative set bias) is now common knowledge in machine learning for malware classification. For these reasons, we have developed a standard for benign datasets to be used toward machine learning in the malware classification domain. We are also releasing a sample benign data set designed to minimize these problems.