The Human Connectome Project (HCP) has developed minimal preprocessing pipelines for structural, functional, and diffusion MRI data to enable cross-subject comparisons and multi-modal analysis of brain architecture, connectivity, and function. These pipelines are designed to capitalize on the high-quality data from the HCP, using a recently introduced CIFTI file format and the associated grayordinates spatial coordinate system. The pipelines aim to remove spatial artifacts and distortions, generate cortical surfaces, segmentations, and myelin maps, and align data to standard space. They also include steps such as field map distortion correction, which are widely accepted to be beneficial but often neglected in practice. The final standard space allows for combined cortical surface and subcortical volume analyses while reducing storage and processing requirements for high spatial and temporal resolution data. The CIFTI file format supports a variety of connectome-specific data representations, including combinations of cortical grey matter data modeled on surfaces and subcortical grey matter data modeled in volumetric parcels. The term "grayordinates" is used to describe the spatial dimension in this combined coordinate system. The standard CIFTI grayordinates space provides more precise spatial correspondence across subjects than volumetrically aligned data and is the desired endpoint of the HCP minimal preprocessing functional pipelines. The HCP rfMRI and tfMRI timeseries data are provided in this space. The CIFTI file format is important for reducing the computational and storage demands of high spatial and temporal resolution data. It combines the left and right cerebral hemispheres and subcortical parcels into a single file, simplifying file management for combined surface and volume analyses and visualization. CIFTI files contain a single 2D matrix, with one dimension representing the spatial domain and the other dimension representing something else. The CIFTI format is currently only supported natively by Connectome Workbench and its commandline utilities. It is also beneficial for reducing the computational and storage demands of high spatial and temporal resolution data. The HCP structural acquisitions include high resolution T1-weighted (T1w) and T2-weighted (T2w) images for the purpose of creating more accurate cortical surfaces and myelin maps. The T1w and T2w images are acquired with optimized contrast parameters and include B0 fieldmaps, B1- and B1+ maps for correcting readout distortion and enabling future correction of intensity inhomogeneity. The HCP functional and diffusion acquisitions are described in detail in other papers of this special issue. The functional data are acquired at 2mm isotropic resolution, which is of unusually high spatial resolution for whole-brain coverage at 3T. The diffusion acquisition is covered in detail in other papers of this special issue. The HCP pipelines include minimum acquisition requirements for the HCP data, along with suggestions for best acquisition practices. The HCP pipelines include three structural pipelines (PreFreeSurfer, FreeSurfer, and PostFreeSurfer), two functional pipelinesThe Human Connectome Project (HCP) has developed minimal preprocessing pipelines for structural, functional, and diffusion MRI data to enable cross-subject comparisons and multi-modal analysis of brain architecture, connectivity, and function. These pipelines are designed to capitalize on the high-quality data from the HCP, using a recently introduced CIFTI file format and the associated grayordinates spatial coordinate system. The pipelines aim to remove spatial artifacts and distortions, generate cortical surfaces, segmentations, and myelin maps, and align data to standard space. They also include steps such as field map distortion correction, which are widely accepted to be beneficial but often neglected in practice. The final standard space allows for combined cortical surface and subcortical volume analyses while reducing storage and processing requirements for high spatial and temporal resolution data. The CIFTI file format supports a variety of connectome-specific data representations, including combinations of cortical grey matter data modeled on surfaces and subcortical grey matter data modeled in volumetric parcels. The term "grayordinates" is used to describe the spatial dimension in this combined coordinate system. The standard CIFTI grayordinates space provides more precise spatial correspondence across subjects than volumetrically aligned data and is the desired endpoint of the HCP minimal preprocessing functional pipelines. The HCP rfMRI and tfMRI timeseries data are provided in this space. The CIFTI file format is important for reducing the computational and storage demands of high spatial and temporal resolution data. It combines the left and right cerebral hemispheres and subcortical parcels into a single file, simplifying file management for combined surface and volume analyses and visualization. CIFTI files contain a single 2D matrix, with one dimension representing the spatial domain and the other dimension representing something else. The CIFTI format is currently only supported natively by Connectome Workbench and its commandline utilities. It is also beneficial for reducing the computational and storage demands of high spatial and temporal resolution data. The HCP structural acquisitions include high resolution T1-weighted (T1w) and T2-weighted (T2w) images for the purpose of creating more accurate cortical surfaces and myelin maps. The T1w and T2w images are acquired with optimized contrast parameters and include B0 fieldmaps, B1- and B1+ maps for correcting readout distortion and enabling future correction of intensity inhomogeneity. The HCP functional and diffusion acquisitions are described in detail in other papers of this special issue. The functional data are acquired at 2mm isotropic resolution, which is of unusually high spatial resolution for whole-brain coverage at 3T. The diffusion acquisition is covered in detail in other papers of this special issue. The HCP pipelines include minimum acquisition requirements for the HCP data, along with suggestions for best acquisition practices. The HCP pipelines include three structural pipelines (PreFreeSurfer, FreeSurfer, and PostFreeSurfer), two functional pipelines