Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory diseases, diarrhea, etc.) is a promising way to improve calf welfare in dairy farms. A classic approach to automatically monitoring behavior is to equip animals with accelerometers attached to neck collars and to develop machine learning models from accelerometer time-series. However, to be used for model development, data must be equipped with labels. Obtaining these labels requires annotating behaviors from direct observation or videos, a time-consuming and labor-intensive process. To address this challenge, we propose the ActBeCalf (Accelerometer Time-Series for Calf Behaviour classification) dataset: 30 pre-weaned dairy calves (Holstein Friesian and Jersey) were equipped with a 3D-accelerometer sensor attached to a neck-collar from one week of birth for 13 weeks. The calves were simultaneously filmed with a camera in each pen. At the end of the trial, behaviors were manually annotated from the videos using the Behavioral Observation Research Interactive Software (BORIS) by 3 observers using an ethogram with 23 behaviors. ActBeCalf contains 27.4 hours of accelerometer data aligned adequately with calf behaviors. The dataset includes the main behaviors, like lying, standing, walking, and running, and less prominent behaviors, such as sniffing, social interaction, and grooming. Finally, ActBeCalf was used for behavior classification with machine learning models: (i)two classes of behaviors, [active and inactive; model 1] and (ii)four classes of behaviors [running, lying, drinking milk, and 'other' class; model 2] to demonstrate its reliability. We got a balanced accuracy of 92% [model1] and 84% [model2]. ActBeCalf is a comprehensive and ready-to-use dataset for classifying pre-weaned calf behaviour from the acceleration time series.