The Human Connectome Project (HCP) has developed an integrated approach to data acquisition, analysis, and sharing, known as the "HCP-style" paradigm. This paradigm is characterized by seven core tenets: (1) collecting multimodal imaging data from many subjects; (2) acquiring data at high spatial and temporal resolution; (3) preprocessing data to minimize distortions, blurring, and temporal artifacts; (4) representing data using the natural geometry of cortical and subcortical structures; (5) accurately aligning corresponding brain areas across subjects and studies; (6) analyzing data using neurobiologically accurate brain parcellations; and (7) sharing published data via user-friendly databases. The HCP has made significant contributions to MRI acquisition protocols, high-quality neuroimaging data, software and informatics tools, and a growing number of intriguing discoveries. The HCP-style paradigm differs from traditional neuroimaging practices, which often involve voxel-based analysis, extensive spatial smoothing, and the use of simplified parcellations. The HCP's approach addresses limitations in spatial fidelity, neuroanatomical fundamentals, and cross-study comparability. The article provides a conceptual overview of the HCP-style paradigm, highlights recent discoveries, and offers guidance for future research. It emphasizes the importance of comprehensive multi-modal data acquisition, optimization of data analysis precision, accurate alignment of brain areas, neuroanatomically accurate maps of brain areas, and routine data sharing with advanced informatics infrastructure.The Human Connectome Project (HCP) has developed an integrated approach to data acquisition, analysis, and sharing, known as the "HCP-style" paradigm. This paradigm is characterized by seven core tenets: (1) collecting multimodal imaging data from many subjects; (2) acquiring data at high spatial and temporal resolution; (3) preprocessing data to minimize distortions, blurring, and temporal artifacts; (4) representing data using the natural geometry of cortical and subcortical structures; (5) accurately aligning corresponding brain areas across subjects and studies; (6) analyzing data using neurobiologically accurate brain parcellations; and (7) sharing published data via user-friendly databases. The HCP has made significant contributions to MRI acquisition protocols, high-quality neuroimaging data, software and informatics tools, and a growing number of intriguing discoveries. The HCP-style paradigm differs from traditional neuroimaging practices, which often involve voxel-based analysis, extensive spatial smoothing, and the use of simplified parcellations. The HCP's approach addresses limitations in spatial fidelity, neuroanatomical fundamentals, and cross-study comparability. The article provides a conceptual overview of the HCP-style paradigm, highlights recent discoveries, and offers guidance for future research. It emphasizes the importance of comprehensive multi-modal data acquisition, optimization of data analysis precision, accurate alignment of brain areas, neuroanatomically accurate maps of brain areas, and routine data sharing with advanced informatics infrastructure.