Modern facial surgical planning and therapeutic strategies rely heavily on the precise segmentation of the nasal cavity and paranasal sinuses from computed tomography (CT) images for quantitative analysis. Nevertheless, manual segmentation is labor-intensive and prone to inconsistencies, highlighting the need for automatic segmentation methods. A significant challenge in this field is the lack of publicly available clinical datasets for research. To address this issue, we introduce NagalSeg, the first large-scale, publicly available dataset for nasal cavity and paranasal sinus segmentation. In comparison to existing nasal structure segmentation datasets, which are either private or small-scale, NagalSeg stands out as the first publicly accessible dataset. It provides an order of magnitude more labeled data, consisting of 130 3D CT scans with pixel-wise annotations of five anatomical structures: the left nasal cavity, right nasal cavity, nasopharynx, left maxillary sinus, and right maxillary sinus. The NagalSeg dataset serves as an open-access resource to facilitate the development and evaluation of segmentation algorithms and promote future in-depth research towards the clinical application of artificial intelligence methods.