dataesr/scientific-paragraphs-categorization huggingface dataset:
At a first glance it does not seem to be a clear relation between the country of origin and acoustic diversity: neither the US music market share nor the domestic market share present a pattern linked to the evolution of acoustic diversity displayed on Figure  3. However, we formally control for the role that the country of origin of the songs entering the Charts could have on acoustic diversity by including in our regressions the domestic market share as well as the US market share for each weekly chart of the ten countries. We thus use Musicbrainz, an online music database that collects music metadata 10  , to document the nationality of the artists. Musicbrainz mainly identifies the nationality of an artist from the "artist area" which indicates the area with which an artist is primarily identified with.
Should only supply-side forces matter, there is no reason to believe that the evolution of acoustic diversity would be different in the top or in the bottom of the charts. Since success is highly uncertain, artists decide ex ante on a creation strategy (if any), based on expected success. Therefore, if only supply-side forces are at work, there should not be any difference ex post between the top and the bottom of the charts in terms of diversity 23. To check for this conjecture, we have constructed a second dataset with only the weekly top 10 and the bottom 10 for each year and each country, and recalculated the average acoustic distance between each pair of songs within each top 10 and each bottom 10. We include a dummy variable top10 that equals 1 if the data refers to the top 10 and 0 if it refers to the bottom 10.
More specifically, we argue that in countries where musical tastes seem to be the more eclectic, the development of IT is associated with a stronger increase of acoustic diversity, since it helps to explore the huge catalogue of recorded music. One of the limitations of this paper stems from our database. We only consider the top 100 songs for each week, and therefore, we are unable to analyze the possible homogenization of music consumption at a more granular level. Let us recall that the weekly charts constitute the bulk of music sales, and when studying the impact of digitization on the disparity of music consumption, it makes sense to focus on the tops. However, studying how acoustic diversity evolves beyond the charts is an interesting avenue for future research. Digitization facilitates entry of music suppliers due to lower costs of production, distribution, and promotion, allowing them to serve niche audiences better.
Results In this section we will discuss both X-ray and ultrasonic interferometry results. It is well accepted that the latter approach is more reliable, in particular because it does not need an external pressure scale such as NaCl EoS (e. g. ,  Kung et al. , 2002). Nevertheless, a comparison of the two datasets provides clues on the consistency of our results. We removed data acquired during the cold-pressing step of our experiment in both X-rays (already presented in  Béjina et al. , 2019)  and sound velocity sets because they present the highest differential stress levels, and these may affect sound velocity measurements. The microstructure of our sample was also described in our previous publication, and we will only emphasize that our fayalite remained stable throughout the experiment.
NaCl peaks were fitted using the software plot85 (Weidner et al. , 2010), while fityk  (Wojdyr, 2010)  was used for Fa peaks. All unit-cell volumes were calculated using both the UnitCell software  (Holland and Redfern, 1997)  and our own set of Python scripts that make use of the LMFIT minimization package  (Newville et al. , 2014) , also used for the stress calculations. Both procedures gave identical volumes within uncertainties, and UnitCell diagnostics helped filter poorly fitting peaks. In the end, unit-cell volumes of NaCl at high pressure and high temperature were calculated from the d spacings of peaks (  200 ), (  220 ), (  222 ), (  400 ), (  420 ) and (  422 ), and for Fa we kept peaks (  061 ), (  112 ), (  130 ), (  131 ), (  132 ), (  133 ), (  134 ), (  152 ), (  211 ), (  222 ), (  241 ) and (422).
Pressure was first estimated from the NaCl volumes using a third-order Birch-Murnaghan (BM) EoS  (Birch, 1947)  with the isothermal bulk modulus, K T , dK T /dP and dK T /dT , from  Brown (1999) , as recommended by  Strässle et al. (2014) , and thermal expansion from  Spetzler et al. (1972). This pressure was then corrected for the differential stresses, t, measured in both NaCl and Fa (Fig. 4 ) following the next relation  (Béjina et al. , 2019) : P Fa = P NaCl - 2 3 (t Fa -t NaCl ), (3) where P Fa is the pressure felt by the Fa sample, P NaCl is the pressure calculated from NaCl volumes, and t Fa and t NaCl are stresses measured in the Fa sample and in NaCl respectively (detailed data are listed in Table  S1 ). Stress was calculated from d spacings measured at different azimuths of the detector array using Eq.
3 (Fig. 5 ). Obtained values of V 0 and K T extrapolated to room pressure are listed in Table  3  as a function of temperature. From these values, an overall K T (0,298) = 129±5 GPa was calculated from a lin-    3 ). ear fit, K T = f (T ), and V (0,298) = 307. 9 ± 0. 2 Å 3 was determined from the modified Holland-Powell equation  (Angel et al. , 2014). Despite the dispersion in the data we found a K T (0,298) value compatible with the EoSFit7 results within the uncertainty, therefore validating the simultaneous fit to the whole dataset. 3. 4 K s , dK s /dP , G and dG/dT for fayalite Following the procedure described, for example, in  Kung et al. (2002)  and using finite strain equations from  Davies and Dziewoński (1975) , we calculated K S0 , G 0 and their pressure derivatives at each temperature (Table  4 ).
This method only uses the density of the sample and sound wave velocities and does not need an external pressure gauge such as NaCl (see also  Li and Zhang, 2005). The formulation of  Kung et al. (2002) , thereafter named K02, is a truncated version of  Davies and Dziewoński (1975) , DD75, this latter giving access to second derivatives of K S and G with respect to pressure. Our data do not have the resolution necessary to obtain reliable values for these, but they nevertheless allow for the calculation of pressure. A comparison of the pressure calculated by the finite strain equations with P Fa (Eq. 3) shows a remarkable agreement (Fig. 6 ). One can also see in Fig. 6  that uncorrected pressure is more discrepant. It is therefore not surprising that fitting G = ρV 2 s and K S = ρV 2 p -4G/3 to the data at each temperature using the NaCl pressure scale gives results very close to those from the finite strain method (Table  4 ).
The line shows the 1 : 1 ratio (not a fit). Grey crosses show P NaCl not corrected for stress. P Fa Temperature V a b c V p V s (GPa) (K) (Å 3 ) (Å) (Å) (Å) (km s -1 ) (km V 0 K T dK T /dP dK T /dT d 2 K T /dP 2 α 0 α 1 (Å 3 ) (GPa) (GPa K -1 ) (10 -5 K -1 ) (10 -4 K -1/ mulations give similar results and that K02 values for K S , dK S /dP , G and dG/dP are nearly identical to those obtained when fitting the data using P Fa. On Fig. 7  we show the fit of K02 data (Table  4 ) as a function of T. Points at the highest temperatures may seem to diverge from the trends, but removing them does not significantly change the results of the weighted least-square fit. The value of K S we calculated at 298 K is very close to the one found for K T. The relation K S /K T = 1 + γ αT , with γ the Grüneisen parameter, α the thermal expansion and T the temperature, implies a K T about 1 % smaller than K S , a difference too small to resolve here because of our uncertainties.
e. , as Fe 2+ stoichiometry tends to 1. However, a compar-Table  4. Adiabatic bulk modulus, shear modulus and their pressure derivatives from fitting finite strain equations at each temperature using the full form of  Davies and Dziewoński (1975) , DD75, or their truncated version from  Kung et al. (2002)   5. Results from a linear least-square fit of data (Table  4 ) with T using the formulations of  Davies and Dziewoński (1975) , DD75, and  Kung et al. (2002) , K02, and compared with fitting the data using P Fa. Uncertainties are 1σ calculated from the covariance of the minimization. K and G are expressed in gigapascals, T in kelvin, and P Fa in gigapascals.
DD75 K02 P Fa K S(0,298) 126. 2 ± 2. 4 127. 16 ± 0. 32 127. 1 ± 0. 6 dK S /dT (10 -2 ) -2. 9 ± 0. 5 -2. 18 ± 0. 05 -2. 02 ± 0. 10 dK S /dP 7. 5 ± 1. 7 6. 52 ± 0. 14 6. 36 ± 0. 11 d 2 K S /dP dT (10 -3 ) 2. 4 ± 3. 1 -1. 24 ± 0. 22 -1. 80 ± 0. 17    5. d 2 K S /dP 2 -1. 1 ± 0. 7 d(d 2 K S /dP 2 )/dT (10 -4 ) -6. 0 ± 13. 0 G (0, ison between wüstite and fayalite should be made with caution because the point-defect populations are very different in the two minerals. FeO is known for its non-stoichiometry allowing for the accommodation of high levels of metal vacancies (V Fe ) which form clusters of Fe 3+ surrounded by V Fe (e. g. ,  Minervini and Grimes, 1999) , clusters that are not present in fayalite (e. g. ,  Nakamura and Schmalzried, 1983). On the other hand, in a non-stoichiometric Fe 2 SiO 4 wadsleyite,  Hazen et al.
3 (Table  2 ). From the relation between K S and K T given in Sect. 3. 4, one would expect K S 131 GPa with K S = 5. 3 at 300 K. The values we deduced from our dataset are K S = 127. 2±0. 3 GPa and K S = 6. 5±0. 1 (Table  5 ). Fixing K S at 5. 3 would increase K S to about 130 GPa, a value very consistent with our estimate of K T (Table  2 ). These values follow the trend K S = f (dK S /dP ) from previous studies (Fig. 8 ) and therefore are in general agreement with these. From this figure, K S and dK S /dP of fayalite seem to fall in a limited range,  [125, 135]  GPa and [4, 6. 5] respectively. It is in general difficult to pinpoint a main reason for low values of K S even when correlated with high K S. Among a variety of reasons, we possibly have the wrong temperature reading of the thermocouple, the effect of oxygen fugacity and hence point defects on the elasticity of the olivine structure, the effect of differential stress  (Yagi et al.
Seismic velocities in Mars' upper mantle In order to visualize the effect of fayalite elastic parameters on P and S wave velocities, we calculated V p and V s using the Python package burnman  (Cottaar et al. , 2014)  with the formalism of Stixrude and Lithgow-Bertelloni (2011) (SLB2011) and data for fayalite (K T 0 = 135 GPa, K = 4. 22, G = 50. 9 GPa, G = 1. 46) and then with our determination of these parameters (Table  5 ). We kept the temperature-dependent parameters of SLB2011 because of the large uncertainties in ours. Figure  9  represents the P and S velocities in Fa as a function of pressure along two areotherms for Mars (profiles A and C of  Bertka and Fei, 1997)  and a geotherm from the CAM2016 model  (Priestley and McKenzie, 2013). We chose this geotherm in order to have a very different temperature profile compared to the two areotherms.
Conclusions The bulk and shear moduli of fayalite presented here are the first to be determined by coupled synchrotron X-ray radiation and ultrasonic interferometry. A fit using the third-order Birch-Murnaghan EoS with K T = 5. 3 (fixed) to the data gives K T = 128. 6 ± 5. 1 GPa at room conditions, whereas K S = 127. 2 ± 0. 3 GPa and K S = 6. 5 were obtained from sound wave velocities. Fixing K S to 5. 3 increases K S to 130 GPa, therefore showing that our K T and K S are compatible within uncertainties. These values of K and K follow the linear trend K = f (K ) defined by previously published data. The shear modulus values of fayalite show less dispersion than the bulk modulus; therefore, the latter will be critical when fitting a mineralogical model to Mars InSight SEIS data. The range of K values for the olivine Fe-Mg solid solution is defined between about 125 and 135 GPa.
The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Probing the Earth: experiments and mineral physics at mantle depths". It is a result of the 17th International Symposium on Experimental Mineralogy, Petrology and Geochemistry, Potsdam, Germany, 21-24 June 2020. Acknowledgements. The high-pressure experiments were performed at hutch X17B2 of NSLS (Brookhaven National Lab. ) thanks to the support of COMPRES. We would also like to thank the two anonymous reviewers for their constructive remarks. Financial support. This research has been supported by the TS8 CNES, focusing on Mars InSight SEIS, and by ANR MAGIS (grant no.
, K02, or the stress-corrected NaCl pressure scale, P Fa. Temperature K S dK S /dP d 2 K S /dP 2 G dG/dP d 2 G/dP 2 (K) (GPa) (GPa) DD75 302 125. 7 ± 2. 8 7. 8 ± 2. 3 -1. 2 ± 0. 5 52. 4 ± 1. 7 2. 2 ± 1. 4 -0. 4 ± 0. 5 373 124. 0 ± 4 7. 7 ± 2. 8 -1. 2 ± 0. 6 51. 5 ± 2. 2 2. 1 ± 1. 7 -0. 4 ± 0. 6 472 123 ± 4 6. 6 ± 2. 9 -0. 6 ± 0. 6 50. 9 ± 2. 5 1. 7 ± 1. 7 -0. 2 ± 0. 5 572 117 ± 5 9. 0 ± 3. 3 -1. 6 ± 0. 7 50. 0 ± 3. 1 1. 8 ± 2. 0 -0. 3 ± 0. 6 673 114 ± 10 9 ± 5 -1. 7 ± 1. 1 47 ± 6 2. 6 ± 3. 2 -0. 5 ± 0. 9 773 105 ± 16 12 ± 8 -3. 2 ± 2. 2 47 ± 9 3 ± 5 -0. 6 ± 1. 2 874 114 ± 17 6 ± 8 -0. 8 ± 1. 2 45 ± 10 3 ± 5 -0. 5 ± 1. 0 K02 302 126. 9 ± 1. 4 6. 6 ± 0. 5 53. 5 ± 0. 8 1. 23 ± 0. 27 373 125. 6 ± 1. 5 6. 4 ± 0. 4 52. 6 ± 0. 9 1. 23 ± 0. 27 472 123. 5 ± 1. 6 6. 2 ± 0. 4 51. 7 ± 0. 9 1. 17 ± 0. 27 572 121.
First, we provide a new high-resolution (∼ 700 years) deep-sea pollen record from the Gulf of Cádiz (site U1386, 36 • 49. 680 N; 7 • 45. 320 W) for TV, which shows a moderate expansion of the Mediterranean forest. We then construct the first global forest pollen database for this period. Our compilation features distinct evolutions for different types of forest, highlighting a strong development of temperate and boreal forest which might have delayed the atmospheric CO 2 increase during TV. Finally, the direct comparison of global simulated forests (iLOVECLIM model) to our pollen database reveals overall consistent temperate and boreal forest evolutions despite model biases, thereby supporting the hypothesis of a significant CO 2 sequestration by middle and high-latitude forests of the Northern Hemisphere shortly after the onset of TV.
First, we analyse a new pollen record (U1386) from the Iberian margin to clarify the vegetation evolution during TV in southern Iberia. This region is a key location in terms of climate because it is affected by low-and high-latitude processes. We then establish the first global pollen database for TV in order to understand terrestrial biosphere evolution at a global scale. The pollen records (including U1386) are analysed in a bioclimatic framework, therefore allowing us to capture the spatial variations in terrestrial biosphere evolution across TV. Finally, we run transient simulations over TV to evaluate the quality of terrestrial biosphere representation by comparison to the database and to improve our understanding of the role of terrestrial biosphere in the carbon cycle.
The same method applied to local δ 18 O b minima and maxima defines the onset of the substages MIS11d at ∼ 422 kyr BP and 11c at ∼ 418 kyr BP. For the cluster analysis, the pollen diagram computed with Psimpoll software  (Bennett, 2000)  was divided into a subset of pollen zones defined by the variation in the percentages of at least two pollen taxa with different ecological affinities  (Birks and Birks, 1980). This visual interpretation is supported by a hierarchical clustering analysis based on the minimisation of the Euclidean distance and constrained by the number of sample depths (chclust in R environment,  Juggins, 2009). The resulting dendrogram defines six pollen zones across TV.
Pollen record selection We conducted an extensive literature review to compile the available marine and lacustrine pollen records covering TV. Only records spanning at least the 423-405 kyr BP period were selected. At a global scale, a total of 19 pollen records (including the present U1386 record) with variable resolution were made available by the scientific community (Pangaea database, European pollen database, Neotoma database). However, we note the existence of a few more pollen records covering TV that are not included in this work due to a lack of data accessibility  (Prokopenko et al. , 2002; Tarasov et al. , 2011; Litt et al. , 2014; Cheng et al. , 2018)  or the use of different metrics such as pollen concentration instead of pollen percentages  (de Vernal and Hillaire-Marcel, 2008).
Data analysis Each pollen record is resampled with a 2 kyr time step in order to build consistent composites. The time step value was chosen in order to optimise the database resolution and minimise oversampling of coarse-resolution records (the majority of pollen records features a mean resolution below 2 kyr, Table  2 ). Pollen records are classified into four forest groups defined according to their main bioclimatic specificity. The euro-Siberian-boreo-American group (ES-BA), as defined by  Braun-Blanquet (1930) , covers boreal and temperate forests. Therefore, both Tibetan Plateau pollen records (Heqing and ZB13-C2) are counted in the ES-BA group, the high altitude compensating for their relatively low latitudes. Although Tenaghi Philippon and Lake Ohrid records are on the geographical boundary with the Mediterranean region, they are also classified as the ES-BA group for they reflect a temperate forest cover.
Model description We use the three-dimensional Earth system model of intermediate complexity iLOVECLIM, a newer version of the LOVECLIM version 1. 2  (Goosse et al. , 2010)  resolving the carbon cycle  (Bouttes et al. , 2015). iLOVECLIM includes an ocean module (3 • × 3 • horizontal grid, 20 vertical levels), a simplified atmospheric module (∼ 5. 6 • × 5. 6 • horizontal grid, on three vertical layers), a biogeochemical module  (Bouttes et al. , 2015 (Bouttes et al. , , 2020) )  and a terrestrial biosphere module (VECODE;  Brovkin et al. , 1997). For each terrestrial grid point, the vegetation module computes two main plant functional types (PFTs), herbaceous (g) and trees (t), and a desert fraction (d) such that their sum equals 1 (i. e. g + t + d = 1). For example, an ice sheet grid point will feature null tree and herbaceous fractions and a desert fraction of 1  (Roche et al.
Global pollen composite records In this section, we take a step back from our regional analysis to investigate the evolution of the terrestrial biosphere at a global scale owing to the new pollen database. Altogether, forest ecosystems represent the largest biomass reservoir during the preindustrial Holocene period  (Prentice et al. , 2011)  and the Last Glacial Maximum  (Crowley, 1995; Prentice et al. , 2011). Additionally, the carbon uptake by modern forests accounts for the large majority of the terrestrial carbon sink  (Pan et al. , 2011). In this respect, forests are reliable proxies for the terrestrial biosphere. Therefore, we postulate that the evolution of the pollen-based forest percentage is representative of terrestrial biosphere evolution  (Brandon et al. , 2020). Figure  5a  features the pollen record database locations and Fig.
6d , 405-423 kyr BP) complementary to Fig. 6c  and including all sequences is proposed, although we mainly focus on Fig. 6c   2 ) classified into four bioclimatic groups defined in the text (red: Subtropical-S; purple: Tropical; yellow: Subtropical-N; blue: euro-Siberian-boreo-American, ES-BA). Stars refer to the pollen records covering TV and unavailable at the time of the study: numbers 20 to 24 correspond to sites ODP646 (de Vernal and Hillaire-Marcel, 2008), GeoB16602  (Cheng et al. , 2018) , Lake  Van (Litt et al. , 2014) , Lake Baikal  (Prokopenko et al. , 2002)  and Lake Biwa  (Tarasov et al. , 2011)  respectively. Colour shading represents the modern biome distribution modelled by multinomial logistic regression (from  Levavasseur et al. , 2012)  on the BIOME 6000 database  (Prentice et al.
Conclusions Our new high-resolution pollen record (IODP U1386) allows us to resolve millennial-scale changes in southern Iberian vegetation cover over TV. This sequence shows a moderate Mediterranean forest expansion during TV (maximum MF pollen percentage of ∼ 50 %) in line with seminal work by  Oliveira et al. (2016)  and also unveils short timescale forest variations at the onset of the interglacial. TV is characterised by a two-step Mediterranean forest growth beginning at ∼ 424 kyr BP lagging behind the marine isotopic interglacial (defined by the global ice volume decrease) by approximately 4 kyr. At a global scale, we assemble the first pollen record database for TV. Despite sparse pollen data, we observe different forest developments varying with latitude and highlighting the strong increase in temperate and boreal forests across the termination.
We expect this new pollen database to open the way for extensive knowledge of terrestrial biosphere-climate interactions during TV. Yet, model-data comparisons remain fundamental to improve simulated forest accuracy in order to achieve a global cover. Here we observe that iLOVECLIM simulated forests feature overall similar but weaker trends than the pollen-based ones. This first assessment of global forest evolution during TV points out different driving mechanisms: a strong impact of ice sheet retreat on forests is observed at high latitudes, while lower latitude forests are probably more influenced by precipitation and temperature changes  (Yin and Berger, 2012). Based on the presented global pollen-based vegetation records and simulation analysis we suggest the following scenario: 1. The strong warming at the onset of MIS11 results both in a strong increase in high-latitude terrestrial biosphere productivity and in a rise in atmospheric CO 2 concen-tration probably driven by ocean physical and chemical degassing processes.
Data availability. The IODP U1386 pollen record documented here is available on the PANGAEA data repository at https://doi. org/10. 1594/PANGAEA. 939160 (Hes et al. ,  2021). Similarly, the global pollen database across Termination V and the iLOVECLIM simulation output variables (tree fraction and carbon stocks) presented here are accessible at https://doi. org/10. 5281/zenodo. 6611054 (Hes, 2022)  and https://doi. org/10. 5281/zenodo. 5592751 (Hes and Bouttes, 2021)  respectively.
Study area The study area is located in the La Laguna catchment, in the Chilean Coquimbo region, close to the Argentinian border (Fig. 1a ). To assess the sensitivity we use data from the Tapado Automatic Weather Station (AWS), a permanent station since 2009 located close to the terminus of the Tapado Glacier at 30 • S, 69 • W, 4306 m a. s. l. (Fig. 1c ). The site shows a complex topographic setting with average (maximum) wind speeds of 4. 2 m s -1 (>15 m s -1 ) in 2017 and little precipitation (<200 mm a -1 ) that arrives as snow during fewer than 10 events per year. Precipitation events mainly occur during the winter season (>90%)  (Rabatel et al. , 2011; Réveillet et al. , 2020). Therefore the area surrounding the AWS is only covered with snow in austral winter. At this elevation, no vegetation is observed.
SWE was measured with a CS725 sensor by Campbell Scientific which passively detects the change in naturally emitted terrestrial gamma radiation from the ground after it passes through snow cover. It provided two independent SWE observations measuring both potassium and thallium gamma rays  (Wright, 2011). The uncertainty given by the manufacturer is ±15 mm from 0 to 300 mm and ±15% from 300 to 600 mm, but differences of up to 82 mm w. e. between gamma ray measurements at ∼300 mm w. e. were measured. The manufacturer suggests that the output with the higher count is generally the most reliable, which were the potassium gamma rays measurements  (Randall, 2018, personal communication). We display both data sets and estimate an accuracy of ±25 mm for this data set.