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Recent studies have demonstrated the feasibility of localizing an attended sound source from electroencephalography (EEG) signals in a cocktail party scenario. This is referred to as EEG-enabled Auditory Spatial Attention Detection (ASAD). Despite the promise, there is a lack of ASAD datasets. Most existing ASAD datasets are recorded from two speaking locations. To bridge this gap, we introduce a new Auditory Spatial Attention (ASA) dataset, featuring multiple speaking locations of sound sources. The new dataset is designed to challenge and refine deep neural network solutions in real-world applications. Furthermore, we build a channel attention convolutional neural network (CA-CNN) as a reference model for ASA, that serves as a competitive benchmark for future studies.