We expect future works to improve the handling of the CO-to-H₂ conversion factor even further. In particular, combining a varying R₂₁ (either observed directly or predicted based on similar observations; Leroy et al. 2022) with an R₂₁-dependent α<sub>CO</sub> prescription (Gong et al. 2020) would allow us to better capture the gas excitation temperature variations, especially in galaxy centers where this effect becomes very pronounced. We also defer a more thorough comparisons between the different α<sub>CO</sub> prescriptions to a subsequent paper.

## Appendix C
### Inclination Corrections

In Sections 2.1.1 and 2.1.2, we introduce inclination corrections on several molecular cloud properties measured at 60–150 pc scales. These corrections represent an important methodological change, as many previous works (including Sun et al. 2018, 2020a; Rosolowsky et al. 2021, using the same data set) have assumed spherical geometry for observations on these spatial scales and, consequently, have not applied such corrections. This spherical approximation assumes that, at the resolution scale, the structure of the molecular ISM is isotropic. While this assumption is common throughout the literature, it is not well-tested. Furthermore, the 60–150 pc resolutions are also comparable to the thickness of the molecular gas disk, which implies anisotropy. In this appendix, we show that there is an inclination dependence in our measurements and motivate specific functional forms for empirical corrections.

The primary motivation for introducing these corrections is that the observed molecular gas properties (such as surface density and velocity dispersion) at ~100 pc scales show apparent correlations with the host galaxy inclination angle. For example, A. Hughes et al. (2022, in preparation) found such trends in the measured properties of CPROPS-identified objects among the PHANGS-ALMA galaxy sample. A parallel examination of the pixel-by-pixel measurements derived from the same data set (Sun et al. 2020a) also reveals a similar trend (see Figure C1, left panel). Since inclination angle is not an intrinsic property of galaxies, the presence of these inclination-related trends signifies systematic biases in the molecular gas measurements derived with both CPROPS-based and pixel-by-pixel approaches.

To quantify these biases, we carry out a modified version of the variable selection analysis in Section 5.2. We suppress the inclination corrections for all molecular cloud properties (i.e., the cos *i* terms in Equations (1)–(13)) while keeping cos *i* in the list of feature variables. This reanalysis yields a new set of power-law predictive models similar to those in Table 3, but many of these models now carry an extra (cos *i*)<sup>β</sup> term. Specifically, in the predictive models for Σ<sub>obj</sub> and Σ<sub>pix</sub>, the powers of the cos *i* terms are close to *β* = −1.0; while in the models for σ<sub>obj</sub> and σ<sub>pix</sub>, they are close to *β* = −0.5. In other words, to eliminate the apparent cos *i* dependence in these models (which should not be present if these models reflect purely physical trends), one would have to multiply cos *i* to the measured surface densities and (cos *i*)<sup>0.5</sup> to the velocity

dispersions. This motivates the correction terms in Equations (3), (4), (8), and (10).

We also inspect the effects of these inclination corrections on the original pixel-by-pixel measurements in Sun et al. (2020a) without doing any cloud population averaging. Figure C1 compares the surface density–velocity dispersion relation before and after applying the inclination corrections. Without these corrections, the median velocity dispersion at given surface densities appears tends to be higher in galaxies with high inclination angles (smaller cos *i*). This trend largely disappears when we apply the inclination corrections to both axes. This result suggests that, despite being motivated by the observed cloud–environment correlations, our adopted inclination corrections can also remove the unphysical inclination dependence in the relationships among molecular cloud internal properties.

Our adopted inclination corrections are empirical and data-driven, but their functional forms have physical implications. For the surface densities, a multiplicative term of cos *i* is the exact correction one would use for disk-like structures with their orientations aligned with the whole galaxy disk. Our proposed interpretation is that the interstellar gas forms filamentary networks, which preferentially align with the large-scale galaxy disk even at ~100 pc scales (Zucker et al. 2018). This preference in orientation should eventually disappear at smaller scales, but evidently, the PHANGS-ALMA observations do not yet reach the transitional spatial scale.

For the velocity dispersions, there are at least two effects that can produce some inclination dependence: (1) contributions from ordered, in-plane motions of the gas (e.g., beam smearing), and (2) anisotropy of the gas velocity dispersion (usually with the in-plane components larger than the vertical component; see, e.g., Jeffreson et al. 2022). Both effects would predict higher velocity dispersions at higher inclination angles, which is consistent with the direction of the adopted correction factor, but neither would call for a specific functional form of (cos *i*)<sup>0.5</sup> for this correction. Alternatively, if one assumes that the velocity structure of the turbulent gas can be described by a line width–size relation of σ<sub>mol</sub>(l) ∝ l<sup>0.5</sup> (e.g., Solomon et al. 1987), and that this relationship still holds at 60–150 pc scales, then the varying line-of-sight depth with inclination (i.e., l ∝ 1/cos *i*) could imply σ<sub>mol</sub> ∝ (cos *i*)<sup>-0.5</sup>, which matches our empirical result.

We stress that all the inclination-dependent trends we identify above are real measurements that only emerge in statistical analysis of many galaxies at similarly high resolution. They imply that the molecular gas structures are clearly anisotropic at 60–150 pc scales. Existing and future surveys at even higher resolution (e.g., in very nearby galaxies or in CO-bright subregions of PHANGS galaxies) can help us extend this analysis to smaller spatial scales, where the transition from anisotropic to isotropic structures presumably occurs. This transitional spatial scale can be further compared with estimates of the gas disk scale height from independent methods (e.g., Koch et al. 2020, Jeffreson et al. 2022).
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Figure C1.** The relationship between molecular gas surface density ($\Sigma_{mol}$) and velocity dispersion ($\sigma_{mol}$) measured pixel-by-pixel at 150 pc scales, without and with inclination corrections (left and right, respectively). Both plots are made using the published data tables in Sun et al. (2020a). The colors of the data points represent the $\cos i$ values of the host galaxies (see colorbar), but have been median-filtered to bring out the overall trend across the parameter space. The four colored lines in each panel show the running median of $\sigma_{mol}$ at fixed $\Sigma_{mol}$ for galaxies in four different inclination bins. The left panel (without inclination corrections) reveals a mild but statistically significant trend of elevated $\sigma_{mol}$ at fixed $\Sigma_{mol}$ in galaxies with higher inclination (i.e., smaller $\cos i$). This trend largely disappears in the right panel (with the inclination corrections applied).</figcaption></figure>

## Appendix D
### Completeness Corrections

In Section 3.2.2, we identify a systematic bias affecting the population-averaged molecular cloud properties. This systematic bias originates from the incomplete CO flux recovery in the PHANGS–ALMA “strict” moment maps and the associated CPROPS catalogs. We introduce a completeness correction to account for this bias, making use of the measured CO flux completeness, $f_{flux}$, and area coverage fraction, $f_{area}$, in each averaging aperture. In this appendix, we present the mathematical derivation of this completeness-correction method.

We assume that the intrinsic CO intensity probability distribution function (PDF) within each averaging aperture follows a lognormal distribution:

$$P(I_{CO}) = \frac{1}{\sqrt{2\pi} \sigma_{int} I_{CO}} \exp\left[-\frac{(\ln I_{CO} - \ln \bar{I}_{CO,int})^2}{2\sigma_{int}^2}\right]. \quad (D1)$$

This assumption is motivated by observational constraints on the CO intensity distribution in nearby galaxies (e.g., Hughes et al. 2013b; Leroy et al. 2016; Egusa et al. 2018; Sun et al. 2018, 2020a). We further assume that the CO emission included in the “strict” moment maps constitutes everything above a threshold intensity $I_{CO,th}$ (i.e., the maps have a sharp, well-defined sensitivity limit), and that the “strict” maps capture all emission along the sightlines with CO detections. Under these assumptions, we can express $f_{flux}$ and $f_{area}$ in terms of $\sigma_{int}$, $\bar{I}_{CO,int}$, and $I_{CO,th}$:

$$\begin{aligned} f_{area} &= \int_{I_{CO,th}}^{+\infty} P(I_{CO}) dI_{CO} \\ &= \frac{1}{2} \left[ 1 - \operatorname{erf} \left( \frac{\ln(I_{CO,th}/\bar{I}_{CO,int})}{\sqrt{2} \sigma_{int}} \right) \right], \end{aligned} \quad (D2)$$

$$\begin{aligned} f_{flux} &= \frac{\int_{I_{CO,th}}^{+\infty} I_{CO} P(I_{CO}) dI_{CO}}{\int_{0}^{+\infty} I_{CO} P(I_{CO}) dI_{CO}} \\ &= \frac{1}{2} \left[ 1 - \operatorname{erf} \left( \frac{\ln(I_{CO,th}/\bar{I}_{CO,int}) - \sigma_{int}^2}{\sqrt{2} \sigma_{int}} \right) \right]. \end{aligned} \quad (D3)$$

Notice that these two relations allow us to inversely solve for $\sigma_{int}$ and $\ln(I_{CO,th}/\bar{I}_{CO,int})$ and express their combinations in terms of the measurable quantities $f_{area}$ and $f_{flux}$:

$$\frac{\ln(I_{CO,th}/\bar{I}_{CO,int})}{\sigma_{int}} = \sqrt{2} \operatorname{erf}^{-1}(1 - 2f_{area}), \quad (D4)$$

$$\sigma_{int} = \sqrt{2} [\operatorname{erf}^{-1}(1 - 2f_{area}) - \operatorname{erf}^{-1}(1 - 2f_{flux})]. \quad (D5)$$

Here, “erf$^{-1}$” stands for the inverse error function.

We then calculate the appropriate correction factors for the population-averaged molecular cloud properties and express them in terms of $f_{area}$ and $f_{flux}$. The three population-averaged properties we consider here are the flux-weighted average cloud surface density, $\langle\Sigma\rangle$ (Sections 2.1.1–2.1.2), the molecular gas clumping factor, $c_{pix}$ (Section 3.2.1), and the flux-weighted average of the reciprocal of freefall time, $\langle t_{ff}^{-1} \rangle$ (Section 6.1):

$$F_{\text{correct},\Sigma} \equiv \frac{\langle\Sigma\rangle_{\text{true}}}{\langle\Sigma\rangle_{\text{obs}}} \propto \frac{\int_{0}^{+\infty} I_{\text{CO}}^2 P(I_{\text{CO}}) dI_{\text{CO}}}{\int_{0}^{+\infty} I_{\text{CO}} P(I_{\text{CO}}) dI_{\text{CO}}} \times \left[ \frac{\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}}^2 P(I_{\text{CO}}) dI_{\text{CO}}}{\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}} P(I_{\text{CO}}) dI_{\text{CO}}} \right]^{-1} = \frac{f_{\text{flux}}}{f_{\text{compl}}(2)}, \quad (D6)$$
$$ F_{\text{correct,c}} = \frac{c_{\text{pix, true}}}{c_{\text{pix, obs}}} \propto \frac{\int_{0}^{+\infty} I_{\text{CO}}^{2} P(I_{\text{CO}}) dI_{\text{CO}}}{\left(\int_{0}^{+\infty} I_{\text{CO}} P(I_{\text{CO}}) dI_{\text{CO}}\right)^{2}} \\ \times \left[ \frac{\left(\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}}^{2} P(I_{\text{CO}}) dI_{\text{CO}}\right) \left(\int_{I_{\text{CO,th}}}^{+\infty} P(I_{\text{CO}}) dI_{\text{CO}}\right)}{\left(\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}} P(I_{\text{CO}}) dI_{\text{CO}}\right)^{2}} \right]^{-1} \\ = \frac{f_{\text{flux}}^{2}}{f_{\text{area}} f_{\text{compl}}(2)}, \qquad (\text{D7}) $$

$$ F_{\text{correct,t}} = \frac{\langle t_{\text{ff}}^{-1} \rangle_{\text{true}}}{\langle t_{\text{ff}}^{-1} \rangle_{\text{obs}}} \propto \frac{\int_{0}^{+\infty} I_{\text{CO}}^{3/2} P(I_{\text{CO}}) dI_{\text{CO}}}{\int_{0}^{+\infty} I_{\text{CO}} P(I_{\text{CO}}) dI_{\text{CO}}} \\ \times \left[ \frac{\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}}^{3/2} P(I_{\text{CO}}) dI_{\text{CO}}}{\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}} P(I_{\text{CO}}) dI_{\text{CO}}} \right]^{-1} = \frac{f_{\text{flux}}}{f_{\text{compl}}(3/2)}, \qquad (\text{D8}) $$

consistent with intrinsic lognormal $I_{\text{CO}}$-PDFs with $\sigma_{\text{int}}$ ranging from 0 to 1.0 dex. This broadly matches the observed $I_{\text{CO}}$-PDF widths of 0.2 to 0.6 dex in subregions of nearby galaxies (e.g., Hughes et al. 2013b; Sun et al. 2018, 2020a) The implied $I_{\text{CO}}$ PDF widths for another ~12% of apertures exceed 1.0 dex, which seem too high to be physical. These apertures tend to have low $f_{\text{area}}$, possibly suggesting that these apertures include true “empty” areas devoid of CO emission, in which case the lognormal PDF assumption is no longer appropriate. Finally, the remaining ~20% of apertures have $f_{\text{flux}} < f_{\text{area}}$. Given the high $f_{\text{area}}$ in most of these regions, it is likely that there is missing CO flux along sight lines with CO detections. We calculate the completeness correction for these apertures by assuming an ad hoc $f_{\text{flux}}$ value equal to $f_{\text{area}}$.

The color trends in Figure D1 indicate that apertures with low $f_{\text{flux}}$ or $f_{\text{area}}$ would require very significant completeness corrections, which means that the CO detections in these apertures are too “unrepresentative” of the underlying cloud population for us to extract reliable statistics. This motivates us to select a subsample of apertures with high $f_{\text{flux}}$ and high $f_{\text{area}}$ for the more careful correlation analyses in Section 5. The selection criteria, $f_{\text{flux}} > 50\%$ and $f_{\text{area}} > 20\%$, are also illustrated in Figure D1. Among all apertures that meet these criteria, the correction factors on $\langle \Sigma \rangle$ have a median value of 0.03 dex and a maximum of 0.2 dex, which means that the uncorrected $\langle \Sigma \rangle$ values are already close to the inferred true

$$ \text{where } f_{\text{compl}}(\beta) = \frac{\int_{I_{\text{CO,th}}}^{+\infty} I_{\text{CO}}^{\beta} P(I_{\text{CO}}) dI_{\text{CO}}}{\int_{0}^{+\infty} I_{\text{CO}}^{\beta} P(I_{\text{CO}}) dI_{\text{CO}}} = \frac{1}{2} \left[ 1 - \text{erf} \left( \frac{\ln(I_{\text{CO,th}}/I_{\text{CO,int}}) - \beta \sigma_{\text{int}}^2}{\sqrt{2} \sigma_{\text{int}}} \right) \right] \\ = \frac{1}{2} [1 - \text{erf}(\beta \text{erf}^{-1}(1 - 2f_{\text{flux}}) - (\beta - 1) \text{erf}^{-1}(1 - 2f_{\text{area}}))] $$

Note that the second steps in Equations (D6)–(D8) are valid because we adopt a constant $\alpha_{\text{CO}}$ within each aperture.

Figure D1 shows the joint distribution of $f_{\text{area}}$ and $f_{\text{flux}}$ for all 3383 hexagonal apertures, with color codes reflecting the amplitude of the derived correction factors $F_{\text{correct,}\Sigma}$ and $F_{\text{correct,c}}$. The $f_{\text{area}}$ and $f_{\text{flux}}$ values for ~68% of apertures are

population average. For the same set of apertures, the correction factors on $c_{\text{pix}}$ have a smaller median of 0.01 dex but a much larger maximum of 0.6 dex. The few apertures with very large correction factors are those with high $f_{\text{flux}}$ but low $f_{\text{area}}$, where a simple lognormal PDF is likely not a good description of the underlying CO intensity distribution.
<figure><img src="image_15.png" /><figcaption>**Figure D1.** Area coverage fraction, $f_{area}$, vs. CO flux recovery fraction, $f_{flux}$, on 150 pc scales within each of the 3383 hexagonal apertures. Black lines show the expected $f_{area}$ vs. $f_{flux}$ relations for sensitivity-limited CO observations given lognormal-shaped intrinsic $I_{CO}$-PDFs (different line styles correspond to lognormal PDFs with $\sigma$ widths ranging from 0 to 1.0 dex). The colors of the data points reflect the amplitude of the appropriate correction factors on $\langle\Sigma_{150\text{pc}}\rangle$ (left) and $c_{\text{pix,150pc}}$ (right) according to Equations (D6) and (D7). A red box highlights the parameter space with $f_{flux} > 0.5$ and $f_{area} > 0.2$, which are the criteria for selecting the subsample of 871 apertures with high-fidelity measurements in Section 5.</figcaption></figure>

## Appendix E
Resolution Dependence

In this work, we derive molecular cloud measurements from the PHANGS–ALMA CO data at four common resolution levels: 60, 90, 120, and 150 pc. The main text focuses on results derived at 150 pc scale so as to cover the full sample of apertures while keeping the presentation succinct. In this appendix, we draw comparisons across all four spatial scales in order to illustrate the scale dependence of the measured molecular cloud properties.

The molecular cloud measurements are available for fewer and fewer apertures/galaxies as the resolution goes from 150 pc to 60 pc (see Leroy et al. 2021a). To control for changes in the aperture/galaxy sample and any associated selection effects, here we focus on a subset of 328 apertures in 15 galaxies with data at all four spatial scales. Besides, the data sensitivity also drops as the beam size decreases, which could also leads to systematic biases in our molecular cloud population statistics. We address this issue by applying completeness corrections to all population-averaged measurements according to the flux completeness and area coverage fraction of the CO moment maps at each resolution (see Section 3.2.2).

Figure E1 illustrates that most molecular cloud properties presented in Figure 4 show some level of resolution dependence. In detail, the average molecular cloud mass and radius both increase strongly as the data resolution gets coarser (the former is most likely driven by the latter). This reinforces the conclusion that the sizes of the CPROPS-identified objects are primarily set by data resolution rather than physical properties of the gas distribution (see Section 5.2). The slope of the $\langle R_{\text{obj}} \rangle$ trend appears sublinear ($\langle R_{\text{obj}} \rangle \propto D_{\text{beam}}^{0.6}$), which seems to suggest that the molecular gas structure is not completely scale-free between 60–150 pc. The average cloud surface density mildly decreases with beam size, as expected from more beam dilution. The average cloud velocity dispersion increases with beam size, but with a power-law slope of ~0.20–0.23, shallower than the line width–size relation for molecular clouds in the Milky Way disk (e.g., Solomon et al. 1987). The average cloud turbulent pressure mildly declines toward coarser resolution, whereas the average virial parameter largely remains roughly constant. Both trends are predictable from their functional relations with cloud mass, size, and velocity dispersion. Finally, as the molecular gas surface density distribution becomes more homogeneous at coarser resolutions, the estimated clumping factor diminishes accordingly.
<figure><img src="image_16.png" /><figcaption>**Figure E1.** Resolution dependence of the molecular cloud population-averaged properties shown in Figure 4. In each panel, the four “violins” represent histograms of the corresponding cloud properties at 60, 90, 120, and 150 pc resolutions. These histograms are made from a common set of apertures for which measurements at all four resolutions are available. The black open square and vertical bar indicate the median value and 16–84 percentile range (assigning equal weight per aperture).</figcaption></figure>

## Appendix F
Machine-readable Tables

We publish our high-level measurements in the form of machine-readable data tables via the PHANGS CANFAR storage.<fnref n="52" /> A permanent copy of the version used in this article is also available via the CANFAR Data Publication Service.<fnref n="53" /> This version includes two types of tables. The first records the hexagonal aperture measurements, and the second presents the radial bin measurements (see Section 3.1). Here, we provide column-by-column descriptions of the hexagonal aperture tables in Table F1. For clarity, we also add links to the relevant sections and equations for each derived quantity. We

do not separately provide column-by-column descriptions for the radial bin tables, but simply note that the set of columns therein are almost identical, except that the radial bin tables lack the RA, DEC, and phi_gal columns.

We plan to keep improving these data tables and release subsequent versions via the same CANFAR storage (see footnote 52). Future versions will cover a larger sample of galaxies and include more measurements derived from other data sets, such as H II regions and stellar populations from the PHANGS–MUSE survey (Emsellem et al. 2022) as well as star clusters from the PHANGS–HST survey (Lee et al. 2022).

<footnote n="52">⁵² https://www.canfar.net/storage/vault/list/phangs/RELEASES/Sun_etal_2022</footnote>

<footnote n="53">⁵³ https://doi.org/10.11570/22.0072</footnote>
<table><caption>Table F1
Column-by-column Descriptions of the Published Machine-readable Tables</caption><thead><tr><th>Column name</th><th>Unit</th><th>Description</th></tr></thead><tbody><tr><td>ID</td><td></td><td>Aperture ID</td></tr><tr><td>RA</td><td>°</td><td>R.A. of the aperture center (Section 3.3)</td></tr><tr><td>DEC</td><td>°</td><td>decl. of the aperture center (Section 3.3)</td></tr><tr><td>r_gal</td><td>kpc</td><td>Deprojected galactocentric radius (Section 3.3)</td></tr><tr><td>phi_gal</td><td>°</td><td>Deprojected azimuthal angle (0=receding major axis; Section 3.3)</td></tr><tr><td>frac_C021_center</td><td></td><td>Fraction of CO (2-1) flux in the central region (Section 2.6, Section 3.3)</td></tr><tr><td>frac_C021_bars</td><td></td><td>Fraction of CO (2-1) flux in the bar region (Section 2.6, Section 3.3)</td></tr><tr><td>V_circ_C021_URC</td><td>km s<sup>-1</sup></td><td>CO-derived circular velocity (Persic+96 model; Section 2.5, Section 3.3)</td></tr><tr><td>e_V_circ_C021_URC</td><td>km s<sup>-1</sup></td><td>Statistical error on CO-derived circular velocity</td></tr><tr><td>beta_C021_URC</td><td></td><td>Logarithmic derivative of CO rotation curve (Persic+96 model; Section 2.5, Section 3.3)</td></tr><tr><td>e-beta_C021_URC</td><td></td><td>Statistical error on logarithmic derivative of CO rotation curve</td></tr><tr><td>Zprime</td><td></td><td>Gas-phase metallicity relative to solar (Equation (B1)-B2)</td></tr><tr><td>alpha_C021_S20</td><td>s M<sub>☉</sub> K<sup>-1</sup> km<sup>-1</sup> pc<sup>-2</sup></td><td>CO (2-1)-to-H<sub>2</sub> conversion factor (fiducial; Sun+20; Equation (B3))</td></tr><tr><td>alpha_C021_N12</td><td>s M<sub>☉</sub> K<sup>-1</sup> km<sup>-1</sup> pc<sup>-2</sup></td><td>CO (2-1)-to-H<sub>2</sub> conversion factor (Narayanan+12; Equation (B5))</td></tr><tr><td>alpha_C021_B13</td><td>s M<sub>☉</sub> K<sup>-1</sup> km<sup>-1</sup> pc<sup>-2</sup></td><td>CO (2-1)-to-H<sub>2</sub> conversion factor (Bolatto+13; Equation (B6))</td></tr><tr><td>&lt;alpha_C021_G20ICO_Xpc&gt;</td><td>s M<sub>☉</sub> K<sup>-1</sup> km<sup>-1</sup> pc<sup>-2</sup></td><td>Flux-weighted mean CO (2-1)-to-H<sub>2</sub> conversion factor (Gong+20 I<sub>CO</sub>-based @ X<sup>a</sup> pc; Equation (B7))</td></tr><tr><td>Sigma_mol</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Region-average molecular gas surface density (PHANGS-ALMA; Section 3.3)</td></tr><tr><td>e_Sigma_mol</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on region-average molecular gas surface density</td></tr><tr><td>Sigma_atom</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Region-average atomic gas surface density (PHANGS-HI; Equation (14), Section 3.3)</td></tr><tr><td>e_Sigma_atom</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on region-average atomic gas surface density</td></tr><tr><td>MtoL_3p4um</td><td>M<sub>☉</sub> L<sup>-1</sup></td><td>Stellar mass-to-light ratio at 3.4 µm (Leroy+21; Section 2.3)</td></tr><tr><td>Sigma_star</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Region-average stellar mass surface density (fiducial; Section 2.3, Section 3.3)</td></tr><tr><td>e_Sigma_star</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on region-average stellar mass surface density (fiducial)</td></tr><tr><td>Sigma_star_3p6um</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Region-average stellar mass surface density (3.6 µm + varying M/L; Equation (15), Section 3.3)</td></tr><tr><td>e_Sigma_star_3p6um</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on region-average stellar mass surface density (3.6 µm + varying M/L)</td></tr><tr><td>Sigma_star_3p4um</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Region-average stellar mass surface density (3.4 µm + varying M/L; Equation (16), Section 3.3)</td></tr><tr><td>e_Sigma_star_3p4um</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on region-average stellar mass surface density (3.4 µm + varying M/L)</td></tr><tr><td>Sigma_SFR</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Region-average SFR surface density (fiducial; Section 2.4, Section 3.3)</td></tr><tr><td>e_Sigma_SFR</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Statistical error on region-average SFR surface density (fiducial)</td></tr><tr><td>Sigma_SFR_FUVW4</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Region-average SFR surface density (GALEX FUV + WISE4; Equation (17), Section 3.3)</td></tr><tr><td>e_Sigma_SFR_FUVW4</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Statistical error on region-average SFR surface density (GALEX FUV + WISE4)</td></tr><tr><td>Sigma_SFR_NUVW4</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Region-average SFR surface density (GALEX NUV + WISE4; Equation (18), Section 3.3)</td></tr><tr><td>e_Sigma_SFR_NUVW4</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Statistical error on region-average SFR surface density (GALEX NUV + WISE4)</td></tr><tr><td>Sigma_SFR_W4ONLY</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Region-average SFR surface density (WISE4 only; Equation (19), Section 3.3)</td></tr><tr><td>e_Sigma_SFR_W4ONLY</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Statistical error on region-average SFR surface density (WISE4 only)</td></tr><tr><td>Sigma_SFR_HaW4</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Region-average SFR surface density (Hα + WISE4; Equation (22), Section 3.3)</td></tr><tr><td>e_Sigma_SFR_HaW4</td><td>M<sub>☉</sub> yr<sup>-1</sup> kpc<sup>-2</sup></td><td>Statistical error on region-average SFR surface density (Hα + WISE4)</td></tr><tr><td>fracA_C021_pix_Xpc</td><td></td><td>Area filling fraction of CO (2-1) detection @ X<sup>a</sup> pc (Section 3.2.2)</td></tr><tr><td>fracF_C021_pix_Xpc</td><td></td><td>Flux completeness of CO (2-1) detection @ X<sup>a</sup> pc (Section 3.2.2)</td></tr><tr><td>corr_I_C021_pix_Xpc</td><td></td><td>Completeness correction on flux-weighted mean cloud surface density @ X<sup>a</sup> pc (Equation (D6))</td></tr><tr><td>corr_c_C021_pix_Xpc</td><td></td><td>Completeness correction on CO (2-1) clumping factor @ X<sup>a</sup> pc (Equation (D7))</td></tr><tr><td>corr_t ff<sup>-1</sup>_pix_Xpc</td><td></td><td>Completeness correction on flux-weighted mean reciprocal of freefall time @ X<sup>a</sup> pc (Equation D8)</td></tr><tr><td>c_C021_pix_Xpc</td><td></td><td>Clumping factor of CO (2-1) emission @ X<sup>a</sup> pc (Equation (24))</td></tr><tr><td>e_c_C021_pix_Xpc</td><td></td><td>Statistical error on clumping factor of CO (2-1) emission @ X<sup>a</sup> pc</td></tr><tr><td>&lt;Sigma_mol_pix_Xpc&gt;</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Flux-weighted mean molecular gas surface density @ X<sup>a</sup> pc (Equation (8), Section 3.2)</td></tr><tr><td>e-&lt;Sigma_mol_pix_Xpc&gt;</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on flux-weighted mean molecular gas surface density @ X<sup>a</sup> pc</td></tr><tr><td>&lt;vdisp_mol_pix_Xpc&gt;</td><td>km s<sup>-1</sup></td><td>Flux-weighted mean molecular gas velocity dispersion @ X<sup>a</sup> pc (Equation (10), Section 3.2)</td></tr><tr><td>e-&lt;vdisp_mol_pix_Xpc&gt;</td><td>km s<sup>-1</sup></td><td>Statistical error on flux-weighted mean molecular gas velocity dispersion @ X<sup>a</sup> pc</td></tr><tr><td>&lt;P_turb_pix_Xpc&gt;</td><td>K cm<sup>-3</sup></td><td>Flux-weighted mean molecular gas turbulent pressure @ X<sup>a</sup> pc (Equation (12), Section 3.2)</td></tr><tr><td>e-&lt;P_turb_pix_Xpc&gt;</td><td>K cm<sup>-3</sup></td><td>Statistical error on flux-weighted mean molecular gas turbulent pressure @ X<sup>a</sup> pc</td></tr><tr><td>&lt;alpha_vir_pix_Xpc&gt;</td><td></td><td>Flux-weighted mean virial parameter @ X<sup>a</sup> pc (Equation (13), Section 3.2)</td></tr><tr><td>e-&lt;alpha_vir_pix_Xpc&gt;</td><td></td><td>Statistical error on flux-weighted mean virial parameter @ X<sup>a</sup> pc</td></tr><tr><td>&lt;t cross<sup>-1</sup>_pix_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Flux-weighted mean reciprocal of crossing time @ X<sup>a</sup> pc (Equation (28), Section 3.2)</td></tr><tr><td>e-&lt;t cross<sup>-1</sup>_pix_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Statistical error on flux-weighted mean reciprocal of crossing time @ X<sup>a</sup> pc</td></tr><tr><td>&lt;t ff<sup>-1</sup>_pix_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Flux-weighted mean reciprocal of freefall time @ X<sup>a</sup> pc (Equation (27), Section 3.2)</td></tr><tr><td>e-&lt;t ff<sup>-1</sup>_pix_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Statistical error on flux-weighted mean reciprocal of freefall time @ X<sup>a</sup> pc</td></tr><tr><td>N_obj_Xpc</td><td></td><td>Number of CPROPS objects in each aperture @ X<sup>a</sup> pc</td></tr><tr><td>fracF_C021_obj_Xpc</td><td></td><td>Flux completeness of CPROPS objects @ X<sup>a</sup> pc (Section 3.2.2)</td></tr><tr><td>&lt;M_mol_obj_Xpc&gt;</td><td>M<sub>☉</sub></td><td>Flux-weighted mean object molecular gas mass @ X<sup>a</sup> pc (Equation (1), Section 3.2)</td></tr><tr><td>e-&lt;M_mol_obj_Xpc&gt;</td><td>M<sub>☉</sub></td><td>Statistical error on flux-weighted mean object molecular gas mass @ X<sup>a</sup> pc</td></tr><tr><td>&lt;Sigma_mol_obj_Xpc&gt;</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Flux-weighted mean object molecular gas surface density @ X<sup>a</sup> pc (Equation (3), Section 3.2)</td></tr><tr><td>e-&lt;Sigma_mol_obj_Xpc&gt;</td><td>M<sub>☉</sub> pc<sup>-2</sup></td><td>Statistical error on flux-weighted mean object molecular gas surface density @ X<sup>a</sup> pc</td></tr><tr><td>&lt;vdisp_mol_obj_Xpc&gt;</td><td>km s<sup>-1</sup></td><td>Flux-weighted mean object velocity dispersion @ X<sup>a</sup> pc (Equation (4), Section 3.2)</td></tr></tbody></table>
<table><caption>Table F1
(Continued)</caption><thead><tr><th>Column name</th><th>Unit</th><th>Description</th></tr></thead><tbody><tr><td>e_&lt;vdisp_mol_obj_Xpc&gt;</td><td>km s<sup>-1</sup></td><td>Statistical error on flux-weighted mean object velocity dispersion @ X<sup>a</sup> pc</td></tr><tr><td>&lt;R_3d_obj_Xpc&gt;</td><td>pc</td><td>Flux-weighted mean object 3D radius @ X<sup>a</sup> pc (Equation (5), Section 3.2)</td></tr><tr><td>e_&lt;R_3d_obj_Xpc&gt;</td><td>pc</td><td>Statistical error on flux-weighted mean object 3D radius @ X<sup>a</sup> pc</td></tr><tr><td>&lt;P_turb_obj_Xpc&gt;</td><td>K cm<sup>-3</sup></td><td>Flux-weighted mean object molecular gas turbulent pressure @ X<sup>a</sup> pc (Equation (6), Section 3.2)</td></tr><tr><td>e_&lt;P_turb_obj_Xpc&gt;</td><td>K cm<sup>-3</sup></td><td>Statistical error on flux-weighted mean object molecular gas turbulent pressure @ X<sup>a</sup> pc</td></tr><tr><td>&lt;alpha_vir_obj_Xpc&gt;</td><td></td><td>Flux-weighted mean object virial parameter @ X<sup>a</sup> pc (Equation (7), Section 3.2)</td></tr><tr><td>e_&lt;alpha_vir_obj_Xpc&gt;</td><td></td><td>Statistical error on flux-weighted mean object virial parameter @ X<sup>a</sup> pc</td></tr><tr><td>&lt;t_cross&#x0303;-1_obj_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Flux-weighted mean reciprocal of object crossing time @ X<sup>a</sup> pc (Equation (28), Section 3.2)</td></tr><tr><td>e_&lt;t_cross&#x0303;-1_obj_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Statistical error on flux-weighted mean reciprocal of object crossing time @ X<sup>a</sup> pc</td></tr><tr><td>&lt;t_ff&#x0303;-1_obj_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Flux-weighted mean reciprocal of object freefall time @ X<sup>a</sup> pc (Equation (27), Section 3.2)</td></tr><tr><td>e_&lt;t_ff&#x0303;-1_obj_Xpc&gt;</td><td>Myr<sup>-1</sup></td><td>Statistical error on flux-weighted mean reciprocal of object freefall time @ X<sup>a</sup> pc</td></tr></tbody></table>

**Note.**

a X = 60, 90, 120, and 150.

## ORCID iDs

Jiayi Sun (孙嘉懿) https://orcid.org/0000-0003-0378-4667

Adam K. Leroy https://orcid.org/0000-0002-2545-1700

Erik Rosolowsky https://orcid.org/0000-0002-5204-2259

Annie Hughes https://orcid.org/0000-0002-9181-1161

Eva Schinnerer https://orcid.org/0000-0002-3933-7677

Eric W. Koch https://orcid.org/0000-0001-9605-780X

Guillermo A. Blanc https://orcid.org/0000-0003-4218-3944

I-Da Chiang (江逢宜) https://orcid.org/0000-0003-2551-7148

Brent Groves https://orcid.org/0000-0002-9768-0246

Daizhong Liu https://orcid.org/0000-0001-9773-7479

Sharon Meidt https://orcid.org/0000-0002-6118-4048

Hsi-An Pan https://orcid.org/0000-0002-1370-6964

Jérôme Pety https://orcid.org/0000-0003-3061-6546

Miguel Querejeta https://orcid.org/0000-0002-0472-1011

Toshiki Saito https://orcid.org/0000-0001-9016-2641

Karin Sandstrom https://orcid.org/0000-0002-4378-8534

Amy Sardone https://orcid.org/0000-0002-5783-145X

Antonio Usero https://orcid.org/0000-0003-1242-505X

Dyas Utomo https://orcid.org/0000-0003-4161-2639

Thomas G. Williams https://orcid.org/0000-0002-0012-2142

Ashley T. Barnes https://orcid.org/0000-0003-0410-4504

Samantha M. Benincasa https://orcid.org/0000-0003-4826-9079

Frank Bigiel https://orcid.org/0000-0003-0166-9745

Alberto D. Bolatto https://orcid.org/0000-0002-5480-5686

Médéric Boquien https://orcid.org/0000-0003-0946-6176

Mélanie Chevance https://orcid.org/0000-0002-5635-5180

Daniel A. Dale https://orcid.org/0000-0002-5782-9093

Sinan Deger https://orcid.org/0000-0003-1943-723X

Eric Emsellem https://orcid.org/0000-0002-6155-7166

Simon C. O. Glover https://orcid.org/0000-0001-6708-1317

Kathryn Grasha https://orcid.org/0000-0002-3247-5321

Jonathan D. Henshaw https://orcid.org/0000-0001-9656-7682

Ralf S. Klessen https://orcid.org/0000-0002-0560-3172

Kathryn Kreckel https://orcid.org/0000-0001-6551-3091

J. M. Diederik Kruijssen https://orcid.org/0000-0002-8804-0212

Eve C. Ostriker https://orcid.org/0000-0002-0509-9113

David A. Thilker https://orcid.org/0000-0002-8528-7340

## References

Accurso, G., Saintonge, A., Catinella, B., et al. 2017, MNRAS, 470, 4750

Amorín, R., Muñoz-Tuñón, C., Aguerri, J. A. L., & Planesas, P. 2016, A&A, 588, A23

Anand, G. S., Lee, J. C., Van Dyk, S. D., et al. 2021, MNRAS, 501, 3621

Aniano, G., Draine, B. T., Gordon, K. D., & Sandstrom, K. 2011, PASP, 123, 1218

Arimoto, N., Sofue, Y., & Tsujimoto, T. 1996, PASJ, 48, 275

Astropy Collaboration, Robitaille, T. P., Tollerud, E., et al. 2013, A&A, 558, A33

Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123

Bacchini, C., Fraternali, F., Iorio, G., & Pezzulli, G. 2019, A&A, 622, A64

Ballesteros-Paredes, J. 2006, MNRAS, 372, 443

Ballesteros-Paredes, J., Hartmann, L. W., Vázquez-Semadeni, E., Heitsch, F., & Zamora-Avilés, M. A. 2011, MNRAS, 411, 65

Barnes, A. T., Glover, S. C. O., Kreckel, K., et al. 2021, MNRAS, 508, 5362

Benincasa, S. M., Tasker, E. J., Pudritz, R. E., & Wadsley, J. 2013, ApJ, 776, 23

Benincasa, S. M., Loebman, S. R., Wetzel, A., et al. 2020, MNRAS, 497, 3993

Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846

Blanc, G. A., Schruba, A., & Evans, N. J. I 2013, ApJ, 764, 117

Blitz, L., & Shu, F. H. 1980, ApJ, 238, 148

Bolatto, A. D., Leroy, A. K., Rosolowsky, E., Walter, F., & Blitz, L. 2008, ApJ, 686, 948

Bolatto, A. D., Wolfire, M., & Leroy, A. K. 2013, ARA&A, 51, 207

Bolatto, A. D., Wong, T., Utomo, D., et al. 2017, ApJ, 846, 159

Boquien, M., Kennicutt, R., Calzetti, D., et al. 2016, A&A, 591, A6

Bovy, J. 2017a, MNRAS, 468, L63

Bovy, J. 2017b, MNRAS, 470, 1360

Brunetti, N., Wilson, C. D., Sliwa, K., et al. 2021, MNRAS, 500, 4730

Burkhart, B., & Mocz, P. 2019, ApJ, 879, 129

Calzetti, D., Kennicutt, R. C., Engelbracht, C. W., et al. 2007, ApJ, 666, 870

Camacho, V., Vázquez-Semadeni, E., Ballesteros-Paredes, J., et al. 2016, ApJ, 833, 113

Chabrier, G. 2003, PASP, 115, 763

Chevance, M., Kruijssen, J. M. D., Hygate, A. P. S., et al. 2020a, MNRAS, 493, 2872

Chevance, M., Kruijssen, J. M. D., Vázquez-Semadeni, E., et al. 2020b, SSRv, 216, 50

Chevance, M., Kruijssen, J. M. D., Krumholz, M. R., et al. 2021, MNRAS, 509, 272

Chung, A., van Gorkom, J. H., Kenney, J. D. P., Crowl, H., & Vollmer, B. 2009, AJ, 138, 1741

Colombo, D., Hughes, A., Schinnerer, E., et al. 2014, ApJ, 784, 3

Colombo, D., Rosolowsky, E., Duarte-Cabral, A., et al. 2019, MNRAS, 483, 4291

Davis, T. A., Gensior, J., Bureau, M., et al. 2022, MNRAS, 512, 1522

De Vis, P., Jones, A., Viaene, S., et al. 2019, A&A, 623, A5

den Brok, J. S., Chatzigiannakis, D., Bigiel, F., et al. 2021, MNRAS, 504, 3221
Dessauges-Zavadsky, M., Richard, J., Combes, F., et al. 2019, NatAs, 3, 1115

Dobbs, C. L., Pringle, J. E., & Duarte-Cabral, A. 2015, MNRAS, 446, 3608

Dobbs, C. L., Rosolowsky, E., Pettitt, A. R., et al. 2019, MNRAS, 485, 4997

Dobbs, C. L., Krumholz, M. R., Ballesteros-Paredes, J., et al. 2014, in Protostars and Planets VI, ed. H. Beuther et al. (Tucson, AZ: Univ. of Arizona Press), 3

Donovan Meyer, J., Koda, J., Momose, R., et al. 2013, ApJ, 772, 107

Egusa, F., Hirota, A., Baba, J., & Muraoka, K. 2018, ApJ, 854, 90

Ellison, S. L., Lin, L., Thorp, M. D., et al. 2021, MNRAS, 501, 4777

Elmegreen, B. G. 2000, ApJ, 530, 277

Emsellem, E., Schinnerer, E., Santoro, F., et al. 2022, A&A, 659, A191

Espada, D., Verley, S., Miura, R. E., et al. 2019, ApJ, 887, 88

Evans, N. J. I, Heiderman, I., & Vutisalchavakul, N. 2014, ApJ, 782, 114

Federrath, C., & Klessen, R. S. 2012, ApJ, 761, 156

Feldmann, R., Gnedin, N. Y., & Kravtsov, A. V. 2012, ApJ, 747, 124

Field, G. B., Blackman, E. G., & Keto, E. R. 2011, MNRAS, 416, 710

Freeman, P., Rosolowsky, E., Kruijssen, J. M. D., Bastian, N., & Adamo, A. 2017, MNRAS, 468, 1769

Fukui, Y., & Kawamura, A. 2010, ARA&A, 48, 547

Fukui, Y., Mizuno, N., Yamaguchi, R., et al. 1999, PASJ, 51, 745

Genzel, R., Tacconi, L. J., Combes, F., et al. 2012, ApJ, 746, 69

Girichidis, P., Offner, S. S. R., Kritsuk, A. G., et al. 2020, SSRv, 216, 68

Glover, S. C. O., & Mac Low, M. M. 2011, MNRAS, 412, 337

Gong, M., Ostriker, E. C., Kim, C.-G., & Kim, J.-G. 2020, ApJ, 903, 142

Grudić, M. Y., Hopkins, P. F., Faucher-Giguère, C.-A., et al. 2018, MNRAS, 475, 3511

Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Natur, 585, 357

Hennebelle, P., & Chabrier, G. 2011, ApJL, 743, L29

Henshaw, J. D., Longmore, S. N., Kruijssen, J. M. D., et al. 2016, MNRAS, 457, 2675

Henshaw, J. D., Kruijssen, J. M. D., Longmore, S. N., et al. 2020, NatAs, 4, 1064

Herrera, C. N., Pety, J., Hughes, A., et al. 2020, A&A, 634, A121

Herrera-Endoqui, M., Díaz-García, S., Laurikainen, E., & Salo, H. 2015, A&A, 582, A86

Heyer, M., & Dame, T. M. 2015, ARA&A, 53, 583

Heyer, M., Krawczyk, C., Duval, J., & Jackson, J. M. 2009, ApJ, 699, 1092

Heyer, M. H., Carpenter, J. M., & Snell, R. L. 2001, ApJ, 551, 852

Hirota, A., Egusa, F., Baba, J., et al. 2018, PASJ, 70, 73

Hughes, A., Meidt, S. E., Colombo, D., et al. 2013a, ApJ, 779, 46

Hughes, A., Meidt, S. E., Schinnerer, E., et al. 2013b, ApJ, 779, 44

Hunter, J. D. 2007, CSE, 9, 90

Ibáñez-Mejía, J. C., Mac Low, M.-M., Klessen, R. S., & Baczynski, C. 2016, ApJ, 824, 41

Imara, N., & Faesi, C. M. 2019, ApJ, 876, 141

Israel, F. P. 1997, A&A, 328, 471

Israel, F. P. 2020, A&A, 635, A131

James, G., Witten, D., Hastie, T., & Tibshirani, R. 2013, An Introduction to Statistical Learning (New York: Springer),

Jeffreson, S. M. R., & Kruijssen, J. M. D. 2018, MNRAS, 476, 3688

Jeffreson, S. M. R., Kruijssen, J. M. D., Keller, B. W., Chevance, M., & Glover, S. C. O. 2020, MNRAS, 498, 385

Jeffreson, S. M. R., Sun, J., & Wilson, C. D. 2022, MNRAS, submitted

Kass, R. E., & Raftery, A. E. 1995, JASA, 90, 773

Kawamura, A., Mizuno, Y., Minamidani, T., et al. 2009, ApJS, 184, 1

Kennicutt, R. C. J 1998a, ARA&A, 36, 189

Kennicutt, R. C. J 1998b, ApJ, 498, 541

Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531

Kim, C.-G., & Ostriker, E. C. 2017, ApJ, 846, 133

Kim, J., Chevance, M., Kruijssen, J. M. D., et al. 2021a, MNRAS, 504, 487

Kim, J.-G., Ostriker, E. C., & Filippova, N. 2021b, ApJ, 911, 128

Kim, J.-G., Kim, W.-T., & Ostriker, E. C. 2018, ApJ, 859, 68

Klessen, R. S., & Glover, S. C. O. 2016, Saas-Fee Advanced Course, 43, 85

Koch, E. W., Chiang, I.-D., Utomo, D., et al. 2020, MNRAS, 492, 2663

Koda, J., Scoville, N., Sawada, T., et al. 2009, ApJL, 700, L132

Koribalski, B. S., Wang, J., Kamphuis, P., et al. 2018, MNRAS, 478, 1611

Krieger, N., Walter, F., Bolatto, A. D., et al. 2021, ApJL, 915, L3

Kroupa, P. 2001, MNRAS, 322, 231

Kruijssen, J. M. D., Schruba, A., Chevance, M., et al. 2019a, Natur, 569, 519

Kruijssen, J. M. D., Dale, J. E., Longmore, S. N., et al. 2019b, MNRAS, 484, 5734

Krumholz, M., & Burkert, A. 2010, ApJ, 724, 895

Krumholz, M. R., Burkhart, B., Forbes, J. C., & Crocker, R. M. 2018, MNRAS, 477, 2716

Krumholz, M. R., & McKee, C. F. 2005, ApJ, 630, 250

Lang, P., Meidt, S. E., Rosolowsky, E., et al. 2020, ApJ, 897, 122

Larson, R. B. 1981, MNRAS, 194, 809

Lee, E. J., Miville-Deschênes, M.-A., & Murray, N. W. 2016, ApJ, 833, 229

Lee, J. C., Whitmore, B. C., Thilker, D. A., et al. 2022, ApJS, 258, 10

Leroy, A., Bolatto, A., Walter, F., & Blitz, L. 2006, ApJ, 643, 825

Leroy, A. K., Walter, F., Brinks, E., et al. 2008, AJ, 136, 2782

Leroy, A. K., Walter, F., Bigiel, F., et al. 2009, AJ, 137, 4670

Leroy, A. K., Bolatto, A., Gordon, K., et al. 2011, ApJ, 737, 12

Leroy, A. K., Lee, C., Schruba, A., et al. 2013, ApJL, 769, L12

Leroy, A. K., Bolatto, A. D., Ostriker, E. C., et al. 2015, ApJ, 801, 25

Leroy, A. K., Hughes, A., Schruba, A., et al. 2016, ApJ, 831, 16

Leroy, A. K., Schinnerer, E., Hughes, A., et al. 2017, ApJ, 846, 71

Leroy, A. K., Sandstrom, K. M., Lang, D., et al. 2019, ApJS, 244, 24

Leroy, A. K., Schinnerer, E., Hughes, A., et al. 2021a, ApJS, 257, 43

Leroy, A. K., Hughes, A., Liu, D., et al. 2021b, ApJS, 255, 19

Leroy, A. K., Rosolowsky, E., Usero, A., et al. 2022, ApJ, 927, 149

Li, H., Vogelsberger, M., Marinacci, F., Sales, L. V., & Torrey, P. 2020, MNRAS, 499, 5862

Lin, L., Ellison, S. L., Pan, H.-A., et al. 2020, ApJ, 903, 145

Liu, L., Bureau, M., Blitz, L., et al. 2021, MNRAS, 505, 4048

Lu, Z.-J., Pelkonen, V.-M., Padoan, P., et al. 2020, ApJ, 904, 58

Makarov, D., Prugniel, P., Terekhova, N., Courtois, H., & Vauglin, I. 2014, A&A, 570, A13

McKee, C. F., & Ostriker, E. C. 2007, ARA&A, 45, 565

McKee, C. F., Parravano, A., & Hollenbach, D. J. 2015, ApJ, 814, 13

Meidt, S. E., Leroy, A. K., Rosolowsky, E., et al. 2018, ApJ, 854, 100

Meidt, S. E., Glover, S. C. O., Kruijssen, J. M. D., et al. 2020, ApJ, 892, 73

Miura, R. E., Espada, D., Hirota, A., et al. 2018, ApJ, 864, 120

Miville-Deschênes, M.-A., Murray, N., & Lee, E. J. 2017, ApJ, 834, 57

Mizuno, N., Rubio, M., Mizuno, A., et al. 2001, PASJ, 53, L45

Muraoka, K., Sorai, K., Miyamoto, Y., et al. 2019, PASJ, 71, S15

Murphy, E. J., Condon, J. J., Schinnerer, E., et al. 2011, ApJ, 737, 67

Murray, N. 2011, ApJ, 729, 133

Murray, N., Quataert, E., & Thompson, T. A. 2010, ApJ, 709, 191

Murugeshan, C., Kilborn, V., Obreschkow, D., et al. 2019, MNRAS, 483, 2398

Nakanishi, H., & Sofue, Y. 2006, PASJ, 58, 847

Narayanan, D., Krumholz, M. R., Ostriker, E. C., & Hernquist, L. 2012, MNRAS, 421, 3127

Oka, T., Hasegawa, T., Sato, F., et al. 2001, ApJ, 562, 348

Olsen, K., Greve, T. R., Narayanan, D., et al. 2017, ApJ, 846, 105

Orr, M. E., Fielding, D. B., Hayward, C. C., & Burkhart, B. 2022, ApJ, 932, 88

Padoan, P., Federrath, C., Chabrier, G., et al. 2014, in Protostars and Planets VI, ed. H. Beuther et al. (Tucson, AZ: Univ. of Arizona Press), 77

Padoan, P., & Nordlund, Å. 2011, ApJ, 730, 40

Pan, H.-A., Fujimoto, Y., Tasker, E. J., et al. 2015, MNRAS, 453, 3082

Pan, H.-A., Schinnerer, E., Hughes, A., et al. 2022, ApJ, 927, 9

Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, JMLR, 12, 2825, http://jmlr.org/papers/v12/pedregosa11a.html

Persic, M., Salucci, P., & Stel, F. 1996, MNRAS, 281, 27

Pettini, M., & Pagel, B. E. J. 2004, MNRAS, 348, L59

Pilyugin, L. S., & Grebel, E. K. 2016, MNRAS, 457, 3678

Pilyugin, L. S., Grebel, E. K., & Kniazev, A. Y. 2014, AJ, 147, 131

Pineda, J. E., Rosolowsky, E. W., & Goodman, A. A. 2009, ApJL, 699, L134

Popping, G., Narayanan, D., Somerville, R. S., Faisst, A. L., & Krumholz, M. R. 2019, MNRAS, 482, 4906

Querejeta, M., Schinnerer, E., Meidt, S., et al. 2021, A&A, 656, A133

Rice, T. S., Goodman, A. A., Bergin, E. A., Beaumont, C., & Dame, T. M. 2016, ApJ, 822, 52

Robitaille, T., & Bressert, E. 2012, APLpy: Astronomical Plotting Library in Python, Astrophysics Source Code Library, ascl:1208.017

Rosolowsky, E., & Leroy, A. 2006, PASP, 118, 590

Rosolowsky, E., Hughes, A., Leroy, A. K., et al. 2021, MNRAS, 502, 1218

S4G team 2010, Spitzer Survey of Stellar Structure in Galaxies IRSA

Saintonge, A., & Catinella, B. 2022, arXiv:2202.00690

Salim, S., Lee, J. C., Janowiecki, S., et al. 2016, ApJS, 227, 2

Salo, H., Laurikainen, E., Laine, J., et al. 2015, ApJS, 219, 4

Sánchez, S. F., Rosales-Ortega, F. F., Iglesias-Páramo, J., et al. 2014, A&A, 563, A49

Sánchez, S. F., Barrera-Ballesteros, J. K., López-Cobá, C., et al. 2019, MNRAS, 484, 3042

Sánchez, S. F., Barrera-Ballesteros, J. K., Colombo, D., et al. 2021, MNRAS, 503, 1615

Sandstrom, K. 2019, z=0 Multiwavelength Galaxy Synthesis IRSA

Sandstrom, K. M., Leroy, A. K., Walter, F., et al. 2013, ApJ, 777, 5

Schmidt, M. 1959, ApJ, 129, 243

Schruba, A., Kruijssen, J. M. D., & Leroy, A. K. 2019, ApJ, 883, 2

Schruba, A., Leroy, A. K., Walter, F., et al. 2012, AJ, 143, 138
Schruba, A., Leroy, A. K., Kruijssen, J. M. D., et al. 2017, ApJ, 835, 278

Schwarz, G. 1978, AnSta, 6, 461

Scoville, N. Z., & Hersh, K. 1979, ApJ, 229, 578

Sheth, K., Regan, M., Hinz, J. L., et al. 2010, PASP, 122, 1397

Silk, J. 1997, ApJ, 481, 703

Solomon, P. M., Rivolo, A. R., Barrett, J., & Yahil, A. 1987, ApJ, 319, 730

Sorai, K., Kuno, N., Muraoka, K., et al. 2019, PASJ, 71, S14

Spilker, A., Kainulainen, J., & Orkisz, J. 2021, A&A, 653, A63

Stuber, S. K., Saito, T., Schinnerer, E., et al. 2021, A&A, 653, A172

Sun, J. 2022, PhangsTeam/MegaTable: PHANGS mega-table pipeline public release, 3.0, Zenodo, doi:10.5281/zenodo.6584841

Sun, J., Leroy, A. K., Schruba, A., et al. 2018, ApJ, 860, 172

Sun, J., Leroy, A. K., Schinnerer, E., et al. 2020a, ApJL, 901, L8

Sun, J., Leroy, A. K., Ostriker, E. C., et al. 2020b, ApJ, 892, 148

Tan, J. C. 2000, ApJ, 536, 173

Teng, Y.-H., Sandstrom, K. M., Sun, J., et al. 2022, ApJ, 925, 72

The pandas development team 2021, Pandas-Dev/Pandas: Pandas, v1.3.4, Zenodo, doi:10.5281/zenodo.5574486

Tibshirani, R. 1996, J. R. Stat. Soc. Series B Stat. Methodol., 58, 267

Treß, R. G., Sormani, M. C., Smith, R. J., et al. 2021, MNRAS, 505, 5438

Ueda, J., Iono, D., Petitpas, G., et al. 2012, ApJ, 745, 65

Utomo, D., Blitz, L., Davis, T., et al. 2015, ApJ, 803, 16

Utomo, D., Bolatto, A. D., Wong, T., et al. 2017, ApJ, 849, 26

Utomo, D., Sun, J., Leroy, A. K., et al. 2018, ApJL, 861, L18

Utreras, J., Blanc, G. A., Escala, A., et al. 2020, ApJ, 892, 94

Vallini, L., Pallottini, A., Ferrara, A., et al. 2018, MNRAS, 473, 271

Verschuur, G. L. 1993, AJ, 106, 2580

Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, NatMe, 17, 261

Vutisalchavakul, N., Evans, N. J. I, & Heyer, M. 2016, ApJ, 831, 73

Walter, F., Brinks, E., de Blok, W. J. G., et al. 2008, AJ, 136, 2563

Williams, T. G., Kreckel, K., Belfiore, F., et al. 2022, MNRAS, 509, 1303

Wilson, C. D. 1995, ApJL, 448, L97

Wolfire, M. G., Hollenbach, D., & McKee, C. F. 2010, ApJ, 716, 1191

Wong, T. 2009, ApJ, 705, 650

Yim, K., Wong, T., Howk, J. C., & van der Hulst, J. M. 2011, AJ, 141, 48

Yim, K., Wong, T., Xue, R., et al. 2014, AJ, 148, 127

Zucker, C., Battersby, C., & Goodman, A. 2018, ApJ, 864, 153