The review by Schinnerer and Leroy provides a comprehensive overview of the current understanding of molecular gas and star formation processes in nearby galaxies, focusing on cloud-scale observations. Key findings include: 1. **Molecular Gas Properties**: Cloud-scale observations reveal that molecular gas surface density, line width, and internal pressure are strongly influenced by the large-scale galactic environment. These properties are consistent with a turbulent medium strongly affected by self-gravity. 2. **Star Formation Timescales and Efficiencies**: Statistical inference from cloud-scale data suggests a cloud collapse time of order the free-fall or turbulent crossing time ($\sim 10-30$ Myr), followed by rapid ($\lesssim 5$ Myr) gas clearing after star formation begins. The star formation efficiency per free-fall time is well-determined over thousands of individual regions at $\epsilon_{\text{ff}} \approx 0.5^{+0.7}_{-0.2}$. 3. **Stellar Feedback**: Multiple observational approaches have constrained the net yield of stellar feedback, which must support the vertical weight of the galaxy disk. Short gas clearing timescales indicate a significant role for pre-supernova feedback in cloud disruption, leaving supernovae to exert a large influence on the larger galaxy, including stirring turbulence, launching galactic-scale winds, and creating superbubbles. 4. **Galaxy Centers**: Central regions of galaxies, such as central molecular zones (CMZs), exhibit extreme conditions with high molecular gas and star formation rates, short dynamical times, and unique molecular gas and feedback properties. The review also discusses the observational progress, including high-resolution CO mapping by ALMA, optical spectral mapping by VLTI/MUSE and Keck, and mid-IR imaging from JWST, which have provided a systematic view of galaxies resolved into their constituent star-forming units. The authors emphasize the importance of homogenizing data and using statistical image characterization to ensure fair comparative analysis across different datasets.The review by Schinnerer and Leroy provides a comprehensive overview of the current understanding of molecular gas and star formation processes in nearby galaxies, focusing on cloud-scale observations. Key findings include: 1. **Molecular Gas Properties**: Cloud-scale observations reveal that molecular gas surface density, line width, and internal pressure are strongly influenced by the large-scale galactic environment. These properties are consistent with a turbulent medium strongly affected by self-gravity. 2. **Star Formation Timescales and Efficiencies**: Statistical inference from cloud-scale data suggests a cloud collapse time of order the free-fall or turbulent crossing time ($\sim 10-30$ Myr), followed by rapid ($\lesssim 5$ Myr) gas clearing after star formation begins. The star formation efficiency per free-fall time is well-determined over thousands of individual regions at $\epsilon_{\text{ff}} \approx 0.5^{+0.7}_{-0.2}$. 3. **Stellar Feedback**: Multiple observational approaches have constrained the net yield of stellar feedback, which must support the vertical weight of the galaxy disk. Short gas clearing timescales indicate a significant role for pre-supernova feedback in cloud disruption, leaving supernovae to exert a large influence on the larger galaxy, including stirring turbulence, launching galactic-scale winds, and creating superbubbles. 4. **Galaxy Centers**: Central regions of galaxies, such as central molecular zones (CMZs), exhibit extreme conditions with high molecular gas and star formation rates, short dynamical times, and unique molecular gas and feedback properties. The review also discusses the observational progress, including high-resolution CO mapping by ALMA, optical spectral mapping by VLTI/MUSE and Keck, and mid-IR imaging from JWST, which have provided a systematic view of galaxies resolved into their constituent star-forming units. The authors emphasize the importance of homogenizing data and using statistical image characterization to ensure fair comparative analysis across different datasets.