We present ENTFAC (Exploratory NavigaTion in Forest for Autonomous Control), a multi-sensor dataset designed to advance research in robotic perception within forest environments. Collected across four natural sites, ENTFAC comprises approximately 5 km of trajectories and 1 h of recordings, covering environments that range from dense forests to more open areas. The dataset includes calibrated intrinsics and extrinsics for 32-channel LiDAR (light detection and ranging), IMU (inertial measurement unit), 2K RGB (red–green–blue) cameras, and multispectral cameras, supporting high-quality multimodal sensor fusion for 3D localization, mapping, and forestry scene analysis. High-resolution mapping ground truth is provided through partial 3D scans obtained with a FARO Focus industrial-grade laser scanner, complemented by persistent global positioning ground truth from RTK (real-time kinematic) GNSS (global navigation satellite system). To demonstrate its utility, we evaluate state-of-the-art 3D SLAM algorithms, including LIO-SAM and POINT-LIO, highlighting their performance in challenging conditions such as rough terrain, dense vegetation, and GNSS-denied environments. The ENTFAC dataset is publicly available at https://github.com/Forestry-Robotics-UC/ENTFAC to support worldwide research in forestry robotics and the development of robust perception systems for complex natural settings.