dataesr/scientific-paragraphs-categorization huggingface dataset:
PAWS 12 CO (1-0) emission line data The PdBI Arcsecond Whirlpool Survey (PAWS) 4  covers the 12 CO (1-0) line emission at 1 arcsec (≈40 pc) resolution across the full disc of M 51  (Schinnerer et al. 2013 ). The survey combines observations from IRAM's Plateau de Bure Interferometer (PdBI) and the IRAM 30 m single dish telescope. We only use the IRAM 30 m observations for this work, as we do not require high spatial resolution. The data reduction is described in  Pety et al. (2013). Observations were carried out in 2010 and the data have a native spatial resolution of 23 arcsec (≈1. 0 kpc) with an 1σ noise level of 16 mK at 5 km s -1 spectral resolution.
High-density EMPIRE emission line data The EMPIRE survey (Jiménez-Donaire et al. 2019) covered the emission of a number of high-density emission lines in the 3 mm regime, such as HNC (1-0), HCN (1-0) or HCO + (1-0), across the entire star-forming disc of a sample of nine nearby spiral galaxies. The survey uses observations 6  carried out with the EMIR receiver at the IRAM 30 m single dish telescope. The science goal of the survey was to take deep and extended intensity maps of high critical density lines tracing the dense gas content in the galaxy. While the J = 1 → 0 transition of 13 CO is also covered in the survey, we only consider the high-density lines HCN (1-0) HNC (1-0), and HCO + (1-0) in our study and rely on our own 13 CO (1-0) observation (since our observations are deeper).
Using a half beam-sized hexagonal grid, we have an oversampling factor 7 of N s = 4. p6. The spectra for all lines, sampled in the aforementioned way, are combined into a final data structure for further analysis. W line S /N line W line /W12 CO (2-1) W line S /N line W line /W12 CO (2-1) ( ) (mK) (mK) (K km s -1 ) (K km s -1 ) (1) (2) (3) (4) (5) (6) (7) (8) (9 We determine the velocity-integrated brightness temperature by integrating masked spectra of individual sight lines. For the innermost part of the galaxy up to a galactocentric radius of a CO scale length of 0. 23r 25  (Leroy et al. 2008; Lisenfeld et al. 2011; Puschnig et al. 2020 ) 8 , we use the 12 CO (2-1) emission line as a prior for masking. For larger radii, where the CO emission becomes faint, we use the 21 cm H I emission line.
Discussion The rich dataset of different CO isotopologues allows us to address a multitude of science questions. First, in Sect. 5. 1 we look at the various trends we find in the CO isotopologue line ratios. We compare them to the literature and then, in Sect. 5. 4, investigate what the observed trends in the line ratios may imply. In Sect. 5. 1 we also look into a particular environmental variation in the line ratio -the arm-interarm differences we find most notably in the 12 CO (2-1)/(1-0) line ratio -and constrain the cause for this variation. The galactic environment appears to have an impact on the line ratio, so we also investigate how environment affects the CO spectral line energy distribution (SLED) in Sect. 5. 5.
Comparing R 21 with previous studies The first questions to address are whether the galaxy M 51 conforms to the trends seen in other galaxies regarding the CO line ratios and what we can learn from such trends. When looking at the 12 CO line ratios, a few peculiarities are evident for M 51. The well-studied line ratio of 12 CO (2-1)/(1-0) (denoted hereafter as R 12 CO 21 ) is rather high, with a luminosity-weighted average value of R 12 CO 21 = 0. 89 +0. 11  -0. 07. The question of how this compares to findings from other studies using different datasets then arises. In their 12 CO line ratio study, comparing literature values of a larger set of spiral disc galaxies, den Brok et al. (  2021   4. For the 12 CO (2-1)/(1-0) line ratio, we find evidence of larger values in the interarm than in the spiral arm.
in Fig. 12  for all the different combinations of the CO (1-0) and CO (2-1) datasets, den  Brok et al. (2021)  found that the cause of the discrepancy comes from substituting the CO (1-0) data and not from substituting CO (2-1) data. This demonstrates that the opposite arm-interarm trend is not an artefact of the new CO (2-1) data taken with the IRAM 30 m telescope as part of this programme and that care should be taken combining datasets from different telescopes and receivers when carrying out measurements of molecular line ratios. To verify if using 12 CO (2-1) with a different sensitivity and flux calibration uncertainty changes the outcome, we substituted the CLAWS CO (2-1) data with the HERACLES CO (2-1) data  (Leroy et al. 2009 ) as an experiment. We also find a lower line ratio (R 12 CO 21 ≈ 0.
7; see Appendix B). Such a dependence of the absolute value on the specific dataset has been discussed in den Brok et al. (  2021 ) by comparing HERACLES, ALMA, IRAM 30 m, and NRO CO (1-0) and CO (2-1) data. A variation of order 20% has been found between observations of the same line from different telescopes. This variation is attributed to uncertainties in the absolute flux calibration of the individual telescopes, which can vary between 5% to 20%. However, the flux calibration generally affects the observational data globally, so while the absolute values may differ, we would not expect to find different galaxy-wide trends when comparing CO ratios using different single-dish telescope data (e. g. den Brok et al. 2021 find the same arm-interarm R 12 CO 21 (i. e. higher values in the interarm region relative to the arm region) trend in M 51 when substituting CLAWS with HERACLES data).
Based on our discussion in Sect. 5. 4, in which we argue that 13 CO abundance variations cannot explain the variation in the different CO isotopologue line ratios, we therefore conclude that most likely changes in the optical depth explain the observed difference in the arm-interarm values for the 13 CO/ 12 CO line ratio. The 12 CO (3-2)/(2-1) line ratio also shows clear azimuthal variations. We see that the line ratio also peaks in the interarm region at spiral phase of ∼150 • (see Fig. 13 ). We note that from the lines analysed in the figure, it is the only one that does not have 12 CO(1-0) in the denominator. The fact that the line ratio also peaks in the interarm region further underlines the presumption that the discrepancy to previous results comes from the use of different 12 CO(1-0) datasets (PAWS and NRO; see discussion in Sect.
Comparing CO line ratios to simulations To better understand what we can learn from specific CO isotopologue lines, we compare our findings to simulations. Peñaloza et al. (2017)  studied the utility of the 12 CO (2-1)/(1-0) line ratio for uncovering the physical and chemical properties of molecular clouds. In their study, they carried out a high resolution smoothed particle hydrodynamics (SPH) simulation of an isolated molecular cloud using the GADGET-2) SPH code supplemented with a model for the time-dependent H 2 and CO chemistry. The results of this simulation were resampled onto a hierarchical Cartesian mesh and then post-processed with the RADMC-3D radiative transfer code  (Dullemond et al. 2012)  to generate synthetic maps of 12 CO (2-1) and (1-0) line emission. We note that the spatial resolution used in the study is on parsec scale, while our observations are on kiloparsec scales.
the IRAM server. 15 The data have been processed following the methodology adopted for the IRAM Large Programmes EMPIRE  (Jiménez-Donaire et al. 2019)  and HERACLES  (Leroy et al. 2009 ). The IRAM repository for Large Programmes provides the following, non-error beam corrected data products: (i) for all lines: the 3D data cubes, the rms and uncertainty maps, and the integrated brightness temperature (moment-0) maps; and (ii) for 12 CO (2-1) and 13 CO (1-0) only: the intensity-weighted velocity (moment-1) maps, the equivalent width maps, and the peak temperature maps. We refer the reader to the Readme file at the IRAM data repository for more detailed information. We ask that when using this dataset or parts of it, this paper be cited.
TableB. 1. Comparison of the 12 CO (2-1)/(1-0) line ratio within the selected positions, 1-6, using either the CLAWS 12 CO (2-1) and PAWS 12 CO (1-0) dataset or the newly acquired IRAM 30 m data from project E02-20. Notes. For the CLAWS/PAWS R 12 CO 21 , we provide the value without (raw) and with error beam correction (based on the error beam contribution indicated in Figure A. 3).
Acknowledgements. J. dB. , F. B. , J. P. , A. T. B. and I. B. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 726384/Empire ). T. S. , E. S. , H. A. P. and T. G. W. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 694343 ). H. A. P. further acknowledges the  Ministry of Science and Technology (MOST) of Taiwan  under grant  110-2112-M-032-020-MY3. The work of AKL is partially supported by the  National Science Foundation  under Grants No. 1615105 ,  1615109  and  1653300. A. U. acknowledges support from the  Spanish  grants  PGC2018-094671-B-I00 , funded by  MCIN/AEI/10. 13039/501100011033and  by " ERDF A way of making Europe ", and  PID2019-108765GB-I00 , funded by  MCIN/AEI/10.
13039/501100011033. E. R. acknowledges the support of the  Natural Sciences and Engineering Research Council of Canada (NSERC) , funding reference number  RGPIN-2017-03987. C. E. acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG) Sachbeihilfe , grant number  BI1546/3-1. SCOG and RSK acknowledge support from the  DFG  via  SFB 881  " The Milky Way System " (sub-projects  B1 ,  B2  and  B8 ) and from the Heidelberg cluster of excellence EXC 2181-390900948 "STRUCTURES: a unifying approach to emergent phenomena in the physical world, mathematics, and complex data", funded by the  German Excellence Strategy. R. S. K. furthermore thanks for financial support from the  European Research Council  via the  Synergy Grant "ECOGAL" (grant   855130 ). J. M. D. K. gratefully acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) through an  Emmy Noether Research Group  (grant number  KR4801/1-1 ) and the  DFG Sachbeihilfe  (grant number  KR4801/2-1 ), as well as from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  via the  ERC Starting Grant MUSTANG  (grant agreement number  714907 ).
TableB. 1. Comparison of the 12 CO (2-1)/(1-0) line ratio within the selected positions, 1-6, using either the CLAWS 12 CO (2-1) and PAWS 12 CO (1-0) dataset or the newly acquired IRAM 30 m data from project E02-20. Notes. For the CLAWS/PAWS R 12 CO 21 , we provide the value without (raw) and with error beam correction (based on the error beam contribution indicated in Figure A. 3). Position R 12 CO 21 CLAWS/PAWS data raw e. b. corrected E02-20 data Interarm Arm 1 2 3 4 5 6 1. 08 ± 0. 03 1. 05 ± 0. 05 1. 15 ± 0. 07 0. 94 ± 0. 07 0. 75 ± 0. 01 0. 77 ± 0. 02 0. 94 ± 0. 03 0. 84 ± 0. 05 0. 91 ± 0. 06 0. 65 ± 0. 05 0. 70 ± 0. 01 0. 74 ± 0. 01 0. 87 ± 0. 02 0. 80 ± 0. 03 0. 90 ± 0. 04 0. 92 ± 0. 05 0. 52 ± 0. 01 0. 63 ± 0. 01.
Analyses of the data set are available in previous studies  (Purcell et al. , 2019; Tanentzap & Lee, 2017; Tanentzap et al. , 2014). Detailed methods about data collection are available in the supplementary materials. We analysed the vegetation structure with a subset of the complete data set (see Supplementary materials): 67 quadrats 25 × 25 cm in size set along four transects that run from the lowest point of the basin and advance upslope to the kettle hole margin. Foliar cover was estimated for each species using the following cover estimates: 0%, 0. 5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 30%, 40%, …, 100%. We restricted the analysis to the 15 most abundant species in the study area for which we sampled traits on at least 20 individuals. These species collectively represented at least 80% of the total of plant cover in each quadrat  (Pakeman & Quested, 2007).
Details about the prior functions and their hyperparameterisation are available in the supplementary materials. We used a Differential Evolution Markov Chain Monte Carlo algorithm (DEzs MCMC, R-package BayesianTools,  Hartig et al. , 2017)  to estimate the posterior distributions of the parameters. For each model, we ran four chains for 4 × 10 5 steps. We initialised the Z matrix, that contains the current and past states of the MCMC chains, with four 1000-step runs of a differential evolution optimiser  (Oberpriller et al. , 2021; R-package DEoptim, Ardia et al. , 2011). Z is the matrix that contains the current and past states of the MCMC chains. By initialising it with the output of DEoptim, this (8)   allows us to start the chains nearer to the optimum and thus avoids massive burn-in. Convergence was assessed through Gelman's multivariate convergence criterion (MPSRF,  Gelman et al.
Assembly model comparison We compared the fits of the calibrated models using two metrics: the Deviance information criterion (DIC,  Gelman et al. , 2014 ) and Nagelkerke's pseudo R 2 metric  (Nagelkerke, 1991) , which lends itself well to models that use a beta distribution and gives an indication of the variance they explain  (Nakagawa & Schielzeth, 2013 ). Nagelkerke's pseudo R 2 was calculated from the ratio of a model's posterior likelihood and the likelihood of the null model (see above). Furthermore, we evaluate the ability of the models to predict species presence/absence by evaluating the area under the curve (AUC) scores of each assembly model (R-package pROC  Robin et al. , 2011).
Data and methodology To test the hypothesis, this paper draws on the ORBIS database by Bureau van Dijk that compiles detailed information, including financials, shareholdings and the locations of subsidiaries from around the globe. In order to identify characteristics of firms that could lead to their identities, we focus on ownership concentration and female directorship. It is important to mention that it is quite challenging to define issues on identity, especially when using secondary data sources such as balance sheet and profit and loss accounts. However, as argued in the derivation of the hypotheses, the paper focuses on two wellestablished corporate governance dimensions, which have hitherto not been linked to tax haven activity of MNEs. For our ownership concentration model, the sample contains over 7,000 MNEs from 12 developed world countries for the year 2016.
Ownership concentration The sample size is very large but across and contains MNEs of a multitude of sizes. This is to capture as much variation in the snapshot as possible. This study does acknowledge that scholars have argued  (Pedersen and Thomsen 1997)  that 'more variation in  ownership patterns [can be expected] for large [than for small] companies' (766). This argument is consistent with the view of  Faccio and Lang (2002)  who indicate that crosscountry differences are less significant among small firms than they are among large ones. However, to get a richer dataset we abstain from restricting the sample to just firms of a large size, as has been done in previous studies on ownership concentration  (Richter and Weiss 2013). The concept of ownership in this study pertains to financial holdings (capital blocks), which may diverge from the voting rights that owners may hold  (Faleye, Mehrotra, and Morck 2005).
With the ORBIS data set, complete shareholder ownership details are not available for a wide range of MNEs. Using HHI in this scenario will result in accurate measures of some MNEs, drawing on complete information, but for a majority of MNEs the measures would be drawn from incomplete information. Previous work has shown that when complete ownership information is available for some firms but not for others, the comparability of the HHI suffers (Sánchez-Ballesta and García-Meca 2007). After taking this into account and going through the data sample available for the study, it has been decided to use a cr4 measure of ownership concentration, i. e. a measure of percentage ownership by the four largest shareholders. There are shortcomings in this measure. One problem that applies to both the largest shareholder method as well as the HHI is that they sometimes do not take into account the possibility that shareholders may act in concert, whether through informal or through formal mechanisms (e.
Appointment of female members of the board of directors The second variable of interest is the appointment of female members to the board of directors. The dataset utilized for this study is focused on the UK and US firms, looking at over 650 firms for the period 2010 to 2018. The variable 'Female Appointments' represents the number of women appointed to the board of directors by an MNE between the years 2010 and 2012. The data is extended up to 2018 in order to measure the long-term effects of these appointments. In order to capture the effect of female representation on the board of directors, this study also utilizes two measures of tax havens, as shown in Table  4.
The relatively stable conditions of the sea ice cover in the Antarctic, observed for almost 40 years, seem to be changing recently. Therefore, it is essential to provide sea ice thickness (SIT) and volume (SIV) estimates in order to anticipate potential multi-scale changes in the Antarctic sea ice. For that purpose, the main objectives of this work are: (1) to assess a new sea ice freeboard, thickness and volume altimetry dataset over 2003-2020 and (2) to identify first order impacts of the sea ice recent conditions. To produce these series, we use a neuronal network to calibrate Envisat radar freeboards onto CryoSat-2 (CS2). This method addresses the impacts of surface roughness on Low Resolution Mode (LRM) measurements. During the 2011 common flight period, we found a mean deviation between Envisat and CryoSat-2 radar freeboards by about 0.
5 cm. Using the Advanced Microwave Scanning Radiometer (AMSR) and the dual-frequency Altimetric Snow Depth (ASD) data, our solutions are compared with the Upward looking sonar (ULS) draft data, some in-situ measurement of the SIMBA campaign, the total freeboards of 6 Operation Ice Bridge (OIB) missions and ICESat-2 total freeboards. Over 2003-2020, the global mean radar freeboard decreased by about -14% per decade and the SIT and SIV by about -10% per decade (considering a snow depth climatology). This is marked by a slight increase through 2015, which is directly followed by a strong decrease in 2016. Thereafter, freeboards generally remained low and even continued to decrease in some regions such as the Weddell sea. Considering the 2013-2020 period, for which the ASD data are available, radar freeboards and SIT decreased by about -40% per decade.
Later, Price et al. [45]  evaluated first CryoSat-2 (CS2) freeboard estimates in the McMurdo sound by comparisons with in-situ data of the New Zealand field campaigns in 2011 and 2013. Overall, they found a positive bias by about 14 cm, indicating a radar echo reflection in the snow layer rather than at the snow/ice interface over fast ice. Within the European Space Agency (ESA) Climate Change Initiative (CCI), Schwegmann et al. [46]  and Paul et al. [47]  computed Envisat and CS2 sea ice freeboard and thickness over the entire Antarctic for the first time (see Sections 2 and 3. 1). This dataset, which ends in 2016, has not been subsequently validated or analysed. Meanwhile, Zwally et al. [48]  first demonstrate the ability of the Ice, Cloud, and Land Elevation Satellite (ICESat) laser altimetry measurements to produce sea ice freeboards at basin scale.
The lack of repeated in-situ missions, as well as the apparent lack of response to climate modifications (until 2016) also certainly explains the greater attraction to the Arctic. Since 2016-2017, things are changing, revealing near-future potential modifications of the Antarctic sea ice system. The sea ice community has since started to push for more works to improve our understanding of climate projections in the Antarctic. In that respect, the current ESA CryoSat+ Antarctic Ocean (CSAO+) project (http://cryosat. mssl. ucl. ac. uk/csao/index. html, (accessed on 1 May 2022)) aims at producing a reference CS2 sea ice freeboard and thickness product, validated against a comprehensive dataset of airborne and in-situ measurements. The present study is exactly conducted within this context. The main objectives are: (1) to present and assess a long-term sea ice freeboard, thickness and volume from altimetry over the Envisat/CryoSat-2 2003-2020 time period and (2) to provide trends and identify first order impacts of the recent conditions.
For that purpose, we first present CryoSat-2 radar freeboards computed from the Threshold First Maximum Retracker Algorithm (TFMRA) and the SAMOSA+ retrackers (cf. Appendix B) and compare with the CCI dataset. Due to the Low Resolution Mode (LRM) of the Envisat altimeter, a calibration on the CryoSat-2 solution is necessary to derive consistent freeboard from Envisat measurements  [56]. The second part shows the calibration method and the resulting Envisat radar freeboards. In the third part, we compare these estimations with in-situ transects of the SIMBA campaign, ULS sea ice drafts measurements, 6 OIB airborne missions and ICESat-2 laser freeboards. The next section proposes a global and regional assessment of the freeboard, thickness and volume annual mean trends over 2003-2020. Finally, some of the main results of this study are discussed in the last part of the paper.
Envisat The Envisat radar freeboards are computed at the LEGOS laboratory using the methodology developed in the framework of the ESA Fundamental Data Records for Altimetry (FRD4ALT) project (https://www. fdr4alt. org/, (accessed on 15 February 2022)) to produce freeboards from the European-Remote Sensing Satellite (ERS) measurements in the Arctic. We use the Low Resolution Mode (LRM) waveforms of the Envisat RA-2 Sensor Geophysical Data Record (SGDR), Level 1b (L1b), version 3 product from ESA (https://doi. org/10. 5270/EN1-85m0a7b, (accessed on 1 January 2022)). To account for the difference of footprint with CS2 e. g. ,  [47, 56] , Envisat freeboards are calibrated on CS2 using a neural network approach (cf. Section 3. 2). Freeboard and thickness data are provided over 2003-2011 (for the six winter months) on a similar monthly EASE2 grid format as CS2.
Advanced Microwave Scanning Radiometer Data We use the Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) unified L3 daily snow depth data in 12. 5 km × 12. 5 km stereopolar grids data available at the NSIDC [https://nsidc. org/data/AU_SI12/versions/1, (accessed in 30 June 2022)]  [61]. In addition, we use a snow depth climatology developed at the University of Bremen (UB) and the Institute of Environmental Physics (IUP) in the context of the CCI from daily AMSR-E and AMSR-2 snow depth data.
ICESat-2 Data Since September 2018, The NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provide high resolution laser altimetry surface elevation measurements from the Advanced Topographic Laser Altimeter System (ATLAS) photon-counting lidar altimeter. e. g. ,  [62] [63] [64]  In this study, we use the ATL10/ICESat-2 L3A sea ice freeboard dataset https://earthdata. nasa. gov/ described in  [65]  (accessed in 30 June 2022).