Recent advances in computer vision and human pose estimation, together with the increasing accessibility of drone technology, have opened new opportunities for datadriven vision-based performance analysis in sports. However, their applications remain underexplored in cross-country (XC) skiing, where research has predominantly relied on wearable sensors in controlled environments. These approaches are difficult to generalize to field environments and mostly inaccessible to the general public, while video analysis models and unmanned aerial vehicles are rapidly improving. Hence, publicly available XC skiing datasets can enable novel and accessible technique feedback and insights for both professional and recreational athletes. In this work, we introduce the Nordic Skiing Dataset (NSD)1https://www.nordicskiingdataset.se/, a publicly available drone-based video data set designed to support pose estimation, gear classification, and feedback generation for skating-style cross-country skiing. The dataset features 149 annotated videos of 12 skiers across different skill levels, from beginners to elite national athletes. The dataset captures various real-world skiing conditions and includes annotated segments for gear classification and technique evaluation. Pose annotations were generated using AlphaPose with the Halpe26 keypoint format and refined. Developed in close collaboration with domain experts from the Swedish national skiing team, NSD was carefully designed to support robust and generalizable machine learning research. Although we present initial benchmark results for gear classification, our primary contribution is the dataset itself, an open resource designed to foster further research in athlete modeling, transfer learning, performance feedback, and sports biomechanics.