Subgroup discovery is a broadly applicable exploratory technique, which identifies interesting subgroups with respect to a property of interest. While there is clearly a need to apply this method to discover interesting patterns from scientific datasets comprising largescale arrays, the existing algorithms primarily apply to relational datasets. In this paper, we present a novel algorithm, SciSD, for exhaustive but efficient subgroup discovery over array-based scientific datasets, in which all attributes are numeric. Our algorithm handles a key challenge associated with array data, which is that a subgroup identified over array data can be described based on value-based and/or dimension-based attributes. To reduce the computational costs, our SciSD algorithm extensively uses bitmap indices (and fast bitwise operations on them). We demonstrate both high efficiency and effectiveness of our algorithm by using multiple real-life datasets.