The Human Connectome Project (HCP) is a collaborative 5-year initiative to map brain connections and their variability in healthy adults. This overview focuses on diffusion MRI (dMRI) and structural connectivity, highlighting recent advances in acquisition and processing that enable high-quality in-vivo MRI data and large-scale subject scanning. The HCP consortium, led by Washington University and the University of Minnesota, aims to provide high-quality data and analysis pipelines for 1200 subjects using multiple imaging modalities, including advanced pulse sequences for diffusion, resting-state functional, and task-based functional MRI, as well as T1 and T2-weighted anatomical scans. The acquisition protocol includes optimized gradient coils, multi-band excitation, and multiple receivers to enhance signal-to-noise ratio (SNR) and reduce scanning time. Preprocessing involves sensitivity encoding for multi-channel diffusion MRI, distortion correction using a model-based approach, and fibre orientation estimation using parametric spherical deconvolution. The final dMRI protocol at 3 Tesla features a multiband factor of 3, partial Fourier factor of 6/8, and isotropic spatial resolution of 1.25mm. The high spatial and angular resolution of the HCP datasets necessitates computational optimization using Graphics Processing Units (GPUs) to speed up processing. The HCP datasets demonstrate improved SNR and specificity in resolving fibre crossings and tractography results, contributing to advancements in macro-connectomics.The Human Connectome Project (HCP) is a collaborative 5-year initiative to map brain connections and their variability in healthy adults. This overview focuses on diffusion MRI (dMRI) and structural connectivity, highlighting recent advances in acquisition and processing that enable high-quality in-vivo MRI data and large-scale subject scanning. The HCP consortium, led by Washington University and the University of Minnesota, aims to provide high-quality data and analysis pipelines for 1200 subjects using multiple imaging modalities, including advanced pulse sequences for diffusion, resting-state functional, and task-based functional MRI, as well as T1 and T2-weighted anatomical scans. The acquisition protocol includes optimized gradient coils, multi-band excitation, and multiple receivers to enhance signal-to-noise ratio (SNR) and reduce scanning time. Preprocessing involves sensitivity encoding for multi-channel diffusion MRI, distortion correction using a model-based approach, and fibre orientation estimation using parametric spherical deconvolution. The final dMRI protocol at 3 Tesla features a multiband factor of 3, partial Fourier factor of 6/8, and isotropic spatial resolution of 1.25mm. The high spatial and angular resolution of the HCP datasets necessitates computational optimization using Graphics Processing Units (GPUs) to speed up processing. The HCP datasets demonstrate improved SNR and specificity in resolving fibre crossings and tractography results, contributing to advancements in macro-connectomics.