dataesr/scientific-paragraphs-categorization huggingface dataset:
Size distribution The aerosol number size distribution was obtained from SMPS, WELAS, and SkyGrimm measurements over different diameter ranges. The measured electrical mobility and optical equivalent diameters from the SMPS and the OPCs were first converted into geometrical diameters (D g ) as described in DB17 and summarized in Table  1. The OPC conversion assumes a dust complex refractive index that in our study was set in the range 1. 47-1. 53 for n and 0. 001-0. 005 for k for both the SkyGrimm and the WELAS (following DB17; for more details see Table  1 ). After conversion, the estimated D g range was 0. 01-0. 50 µm for the SMPS, 0. 65-73. 0 µm for the WELAS, and 0. 29-68. 2 µm for the Sky-Grimm. Due to a calibration issue, data for the SkyGrimm in the range D g > 1 µm were discarded so that the WELAS is the only instrument considered in the super-micron range.
A very low counting efficiency was observed for the WELAS below 1 µm, and data in this size range were also discarded. The SMPS, WELAS, and SkyGrimm data were combined, as detailed in DB17, to obtain the full size distribution of the dust aerosols suspended in the CESAM chamber, (dN/d log D g ) CESAM , and the size distribution behind SW optical instruments inlets, (dN/d log D g ) SWoptics , after taking into account particle losses along sampling lines (see Fig. S1 ). As previously discussed, due to the particle losses in  the sampling line from the chamber to the nephelometer and the aethalometer, the (dN/d log D g ) SWoptics size distribution is cut at 10 µm, so no particles above this diameter reach the SW instruments. The measured size distributions, (dN/d log D g ) CESAM and (dN/d log D g ) SWoptics , were used to estimate the mass concentration of aerosols and their effective diameter (D eff ) in the CESAM chamber and behind the SW instrument inlets as mass concentration = π 6 D 3 g dN d log D g ρ • d log D g , (3) D eff = D 3 g dN d log D g d log D g D 2 g dN d log D g d log D g.
The application of Eq. (  7 ) avoids any bias in the calculated averaged SSA for different soils due to size effects. For two of the analyzed samples (Tunisia and Namib-2), however, the linear fitting procedure was not applicable due to the fact that only two absorption measurements and one absorption measurement, respectively, from the aethalometer were available just after the peak of the injection, with no data afterwards. Average SSA data for Tunisia were thus estimated as the mean of the two available SSA 10 min data points, while the single SSA 10 min measurement at the peak of the injection was reported for Namib-2. This difference in time sampling should be kept in mind when comparing data for these two samples to the rest of the dataset.
Results A total of 19 soil samples from different desert areas in northern Africa, Sahel, eastern Africa and the Middle East, eastern Asia, North America, South America, southern Africa, and Australia were selected for experiments from a collection of 137 soil samples from source areas worldwide. The main information on the provenance of these soils is provided in Table 2. The 19 selected soils, the same as analyzed in DB17, represent the major dust source regions depicted in  Ginoux et al. (2012). Amongst the database of 137 samples from all the world regions that constitute significant dust emitters, the range in mineralogical composition obtained for the chosen samples represents the largest variability in iron oxide contents that can be found worldwide. This is illustrated in Fig. 2  where we represent the variability of hematite and goethite content in the 19 selected soils and compare it with the range of variability of the global desert soils from the database of  Journet et al.
, 2003; Ryder et al. , 2013b Ryder et al. , , 2018;; Jeong et al. , 2014; Denjean et al. , 2016b). It should be keep in mind that field data are also often affected by inlet restrictions so that they cannot measure the whole coarse dust fraction (see Table  1   et al. , 2018). The lowest cutoffs for field data shown in Fig. 4  are for the NAMMA and PRIDE datasets and correspond to upper size limits at 5 and 10 µm in diameter, respectively. These values being above our cutoff of 3. 9 µm means that the comparison with our size dataset is meaningful within the range of our measurements. Only the data from AER-D did not suffer from significant inlet restrictions, thus leading to the observation of giant dust particles up to tens of microns in the Saharan Air Layer off the coasts of western Africa.
7 % (Bodélé Depression) and 5. 8 % (Niger). These data are in the range of values reported in the literature  (Reid et al. , 2003; Scheuvens et al. , 2013; Formenti et al. , 2011 Formenti et al. , , 2014a)). Table  3. Chemical characterization of the dust aerosols in the PM 10. 6 size fraction. Column 3 shows MC Fe% , the fractional mass of elemental iron with respect to the total dust mass concentration (±10 % relative uncertainty), and column 4 reports MC Fe-ox% , the mass fraction of iron oxides with respect to the total dust mass concentration (±15 % relative uncertainty) and its speciation in hematite MC Hem% and goethite MC Goeth% (< ±10 % relative uncertainty). The iron oxide measurements were not made on the Taklimakan sample. Mean values and standard deviations (SDs) based on single-sample data are reported for the full dataset.
Spectral complex refractive index and SSA for the different source regions and comparison to literature data Figures 6 and 7 show the experiment-averaged n, k, and SSA between 370 and 950 nm for the 19 aerosol samples analyzed in this study. Data for n, k, and SSA and their uncertainties are reported in Tables  4  and 5  for each sample together with the average values for each of the eight different source regions and for the full dataset. Figures  6  and 7  show that there are significant differences, both in magnitude and spectral shape, between the imaginary refractive index and SSA for the different samples. The highest values of k (0. 0048-0. 0088 at 370 nm and 0. 0012-0. 0021 at 950 nm) and lowest values of SSA (0. 70-0. 75 at 370 nm and 0. 95-0. 97 at 950 nm) are obtained for the Niger, Mali, Namib-2, and Australia samples, which also show the highest values of both the iron oxide content between 3.
8  and Tables  4  and 5 ). The comparison between the full envelope of n, k, and SSA in this study with literature data is also shown in Fig. 8. Literature values considered for comparison include estimates from ground-based, aircraft, and satellite observations, laboratory studies, AERONET inversions, and estimates from mixing rules based on the dust mineralogical composition. Given that the sample selection in our experiments fully envelopes the global variability of the mineralogy of natural dust, we could expect that our dataset would also fully envelope the global-scale variability of the dust absorption and scattering properties in the SW. When comparing with available literature data we found that our n and SSA datasets encompass the range of values indicated in the literature very well, with only a few outlier points.
In contrast, for the imaginary refractive index the reported range of variability from the literature is significantly larger than that found in our study, with our range of k being mostly at the lower bound of previous results. Nonetheless, our range of k values fully envelopes the ensemble of remote sensing and field campaign data on airborne dust from the previous literature reported in Fig. 8a. The global average spectral values for k in our study (thick black line) perfectly match the  Dubovik et al. (2002)  dataset from a synthesis of AERONET observations from various locations worldwide. Likewise, our k average is also very close to the dataset by  Balkanski et al. (2007)  estimated from mineralogical composition assuming 1. 5 % (by volume) hematite in dust, a value shown to allow for a reconciliation of climate modeling and satellite observations of the dust direct SW radiative effect.
By comparison, the average dust hematite content for the ensemble of our analyzed samples is 1. 8 % (in mass), close to the 1. 5 % value proposed by  Balkanski et al. (2007). Looking at Fig. 8 , the datasets that show the largest values, which also fall outside our estimated range of k over the entire considered wavelength range, are the ones by  Wagner et al. (2012)  obtained from laboratory chamber experiments, especially deviating below the 600 nm wavelength from our range of k, and the ones by  Volz (1972) ,  Patterson et al. (1977), and Hess et al. (1998; i. e. , the OPAC 3. 1 version database, which is the same k dataset used in the new OPAC 4. 0 version;  Koepke et al. , 2015). The  Volz (1972) ,  Patterson et al. (1977) , and OPAC datasets are amongst the most com-monly used references for the dust imaginary refractive index in many climate models.
Nonetheless, their approach differs in various aspects from the one applied here and this can lead to the observed differences in the retrieved k. First, the aerosol generation technique is different between the two works, and this possibly leads to particles with different physicochemical features compared to our study. In  Wagner et al. (2012)  the dust aerosol was generated by a rotating Table  4. Real (n) and imaginary (k) parts of the refractive index estimated for the 19 analyzed dust samples and mean values calculated for the eight regions and for the full dataset. Data for single soils are reported as experiment-averaged values, and their uncertainty is calculated as indicated in Table  1. Mean values and standard deviations (SDs) at each wavelength based on single-sample data are reported for the eight regions and the full dataset.
The median and 10 % and 90 % percentile are also reported for the full dataset. For North America and Australia, for which only one dust sample was analyzed, the reported data correspond to the single sample available from these regions. For the real part, the average over the whole shortwave range (n SW ) is indicated. Mean (± SD), median, and 10 % and 90 % values are indicated in bold font. The region acronyms are as follows. NAF-S: northern Africa-Sahara, SAH: Sahel, EAF-ME: eastern Africa and the Middle East, EA: eastern Asia, NAM: North America, SAM: South America, SAF: southern Africa, AUS: Australia. brush disperser using only the 20-75 µm sieved fraction of the soils. This system acts to disaggregate the finest particles of the soil by passing it through a nozzle. Then the largest aerosol grains were removed by a cyclone system (50 % cutoff at 1.
(1972 ), Patterson et al. (1977) , and the OPAC database  (Hess et al. , 1998; Koepke et al. , 2015). In their study,  Miller et al. (2014)  state that the values of  Dubovik et al. (2002)  from AERONET,  Patterson et al. (1977)  for far-traveled dust, and OPAC probably encompass global solar absorption by dust. In contrast, our results indicate that dust absorption is lower than previously thought, and its average is close to the values reported by  Dubovik et al. (2002)  from AERONET observations and  Balkanski et al. (2007)  for dust with a 1. 5 % volume fraction of hematite. Our range of variability of an order of magnitude for k and between 4 % and 30 % for the spectral SSA is actually large enough to change the sign of the global dust direct effect at the TOA  (Miller et al. , 2004) , as well as its regional implications (e.
e. , D eff,coarse up to 15 µm;  Ryder et al. , 2013b)  and in long-range transport conditions (when most of the coarse particle fraction above a few micrometers has settled out, i. e. , D eff,coarse of 2-3 µm or lower;  Denjean et al. , 2016b). We point out, however, that the use of mineralogy to estimate k and SSA based on linear relationships, as obtained in our study, requires the model-predicted dust composition to accurately reflect that of natural atmospheric aerosols. For this aim, realistic soil mineralogy databases and accurate modeling of the soil to aerosol size fractionation need to be developed in model schemes. In this sense we mention the EMIT project (Earth Surface Mineral Dust Source Investigation) as a potential near-future source of high-resolution surface mineralogy data for arid and semiarid regions based on imaging spectroscopy satellite data  (Green et al.
(1972)  data are for rainout dust collected in Germany; Patterson et al. (1977)  data are for Saharan dust; Hess et al. (1998)  data are from the OPAC database;Colarco et al. (2002)  data are for dust from Dakar, SAL, and Tenerife; Dubovik et al. (2002)  include data from the Bahrain-Persian Gulf and Solar Village-Saudi Arabia AERONET stations;Haywood et al. (2003)  include dust from Mauritania; Sinyuk et al. (2003)  data are from Cape Verde, Dakar, and Burkina Faso; Clarke et al. (2004)  include Asian dust offshore of China, Japan, and Korea; Linke et al. (2006)-A includes dust from Cairo; Linke et al. (2006)-B includes dust from Morocco; Balkanski et al. (2007) calculated data from the mineralogical composition assuming a 1. 5 % hematite mass fraction in dust; Todd et al. (2007) data are from Bodélé; Osborne et al.
Acknowledgements. The  RED-DUST  project was supported by the  French national program LEFE/INSU  and by the  OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie)  through dedicated research funding. The authors acknowledge the  CNRS-INSU  for supporting the CESAM chamber as a national facility and the AERIS data center (https: //www. aeris-data. fr,last access: 16 December 2019) for distributing and curing the data produced by the CESAM chamber through the hosting of the EUROCHAMP data center. This work has received funding from the  European Union 's  Horizon 2020 research and innovation program  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement no. 730997. Claudia Di Biagio  was supported by the  CNRS  via the Labex L-IPSL, funded by the  ANR  (grant no.
ANR-10-LABX-0018 ). Konrad Kandler is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ;  264907654 ,  416816480 ;  KA 2280 ). Field sampling in  Saudi Arabia  was supported by a grant from  King Saud University. The authors thank the LISA staff, who participated in the collection of the soil samples from  Tunisia, Niger, Atacama, Patagonia , and the  Gobi desert  used in this study, and  Sandrine Caquineau  (LOCEAN),  Servanne Chevaillier  (LISA), and  Gautier Landrot  (synchrotron SOLEIL) for their contribution to the XRD, WD-XRF, and XANES analyses. Claudia Di Biagio  thanks  Patrick G. Stegmann  for providing the corrected refractive index data shown in Fig. 8. The authors also wish to acknowledge  Claire Ryder  and  Carlos Pérez Garcia-Pando  for providing valuable comments that helped to increase the readability and quality of the paper.
Financial support. This research has been supported by  Horizon 2020  (grant no. EUROCHAMP-2020 (730997) ), the  Agence Nationale de la Recherche  (grant no. ANR-10-LABX-0018 ), and the  Deutsche Forschungsgemeinschaft  (grant nos. 264907654 ;  416816480 (KA 2280 )). Review statement. This paper was edited by  Patrick Chuang  and reviewed by  Claire Ryder  and  Carlos Pérez García-Pando.
Code availability. The following IDL routines were used in the analysis: mpfitexy. pro (available at http://purl. org/mike/mpfitexy, last access: 16 December 2019) was used to linearly fit data, taking into account uncertainties on both x and y  (Williams et al. , 2010). The MPFITEXY routine depends on the MPFIT package  (Markwardt, 2009) ; mie_single. pro (available at http: //www. atm. ox. ac. uk/code/mie/mie_single. html;  McGarragh et al. , 2019)  was used for optical calculations using Mie theory; and mpcurvefit. pro (available at http://cow. physics. wisc. edu/~craigm/ idl/idl. html; Markwardt, 2019)  was used for size lognormal fitting. Data availability. Complex refractive index and single-scattering albedo data for the different analyzed samples are provided in Tables 4 and 5 and will be compiled together with aerosol properties from other studies within the Library of Advanced Data Products (LADP) of the EUROCHAMP data center (https://data.
eurochamp. org/data-access/optical-properties/, Di Biagio et al. , 2019a, b). The CESAM data used in this study are immediately available upon request to the contact author and will also soon be made available through the Database of Atmospheric Simulation Chamber Studies (DASCS) of the EUROCHAMP data center (https://data. eurochamp. org/data-access/chamber-experiments/, Di  Biagio et al. , 2019c).
Third, successful colonisation across the climatic hinge may be limited by factors such as environmental differences (e. g. changing levels of water stress). If this was the case, we would expect ecological niches of populations either side of the hinge to be significantly different. At present, little is known about the relative importance of these three scenarios so we aim to identify which, if any, have contributed to this common genetic discontinuity. The phylogeographic approach  (Avise et al. 1987 ) can unravel the history of populations, and ultimately uncover which processes have shaped current patterns of diversity. It is therefore an ideal framework to study how the climatic inversion, and other factors, have shaped patterns of intraspecific diversity in CAR plants. High-throughput sequencing allows the generation of phylogeographic datasets consisting of large numbers of independently segregating nuclear loci that can account for coalescent stochasticity  (Edwards et al.
Sample collection A total of 112 individuals of Annickia affinis were sampled across most of the species distribution range in Central Africa (Table  S1 ). In addition, two individuals were sampled from the sister species Annickia polycarpa as outgroups  (Couvreur et al. 2019). We obtained data on flowering and fruiting times of Annickia affinis using herbarium specimens of A. affinis retrieved from the BRAHMS database of Naturalis NL. All specimens studied in the taxonomic revision of Annickia  (Versteegh & Sosef, 2007)  were included in BRAHMS, providing a good representation of available specimens for the genus.
Read filtering, contig assembly and multi-sequence alignment Reads were cleaned and filtered following the protocol in  Couvreur et al. (2019)  and Hybpiper  (Johnson et al. 2016)  was used to prepare sequence data for phylogenetic inference. Alignments were conducted using MAFFT  (Katoh and Standley 2013)  and cleaned with GBLOCKS  (Castresana 2000). Putative paralogs for A. affinis that were flagged by Hybpiper were verified and removed during this process. Further information can be found in the supplementary methods.
SNP calling We used  SeCaPr (v1. 1. 4; Andermann et al. 2018)  to call SNPs as it generates a psuedoreference made up of the consensus sequences for each target locus (paralogs removed) that is tailored to the given dataset, which is more efficient than the bait kit reference made from distantly related Annonaceae species. We then mapped our cleaned, paired reads to this psuedoreference using  BWA (v0. 7. 12; Li and Durbin 2009). Duplicates were removed and SNPs were called using HaplotypeCaller in  GATK (v4. 0; McKenna et al. 2010). We used bcftools (v1. 8; Li 2011) to apply thresholds of mapping quality (>40%) depth (>25), quality by depth (>2), minimum depth across individuals (>10) to filter SNPs. We also removed those SNPs with a minor allele frequency < 0. 01, kept only biallelic SNPs and excluded monomorphic sites.
Population genetic clustering and statistics We examined the genetic structure of A. affinis using three approaches. First, we undertook a Discriminant Analysis of Principal Components (DAPC)  (Jombart et al. 2010). We used the function find. clusters in the R package 'adegenet'  (Jombart 2008)  to identify clusters using successive K-means with 100,000 iterations per value of k and a maximum k value of 20. We identified the most appropriate number of clusters by examining the change in Bayesian Information Criterion (BIC) with increasing values of k (number of clusters). We then used the function dapc in order to define the diversity between the groups identified using find. clusters. We performed cross-validation of our DAPC analysis to ensure our chosen number of PCs was reliable. The data were divided into training (90%) and validation (10%) sets and DAPC was carried out on the training set, with different numbers of PCs retained.
30 replicates were performed to identify the optimal number of PCs -the number that minimizes root mean squared error. We used cluster membership inferred using DAPC to define populations for downstream analyses. Second, we used TESS3 -an approach that takes into account geographic location information when inferring population clusters  (Caye et al. 2016). TESS3 was implemented using the R package 'tess3r'  (Caye et al. 2016). We used the projected least squares algorithm and a maximum k of 20. We examined the cross-validation score for each value of K to identify the appropriate number of clusters. Third, we used fastSTRUCTURE  (Raj et al. , 2014)  on a reduced set of unlinked SNPs (one per locus). To maximize the number of SNPs we did not apply a depth-by-individual filter for fastSTRUCTURE analyses.
Phylogenetic inference We filtered our dataset by choosing only those exons that had 75% of their length reconstructed in 75% of A. affinis individuals. We then used the corresponding supercontigs (i. e. targeted regions and surrounding off-target sequences) for phylogenetic inference. We added empty sequences when individuals were missing from locus alignments and we concatenated loci with the pxcat function from phyx  (Brown et al. 2017). We assigned a different GTR+GAMMA model to each locus to account for differences in substitution rates. We then ran RAxML (v8. 2. 9) (Stamatakis 2014) using the '-f a' option with 100 replicates. The tree was rooted using A. polycarpa as outgroup. We also conducted a coalescent-based phylogenetic analysis using  ASTRAL-III (v5. 5. 11; Zhang et al. 2017) , which uses individual gene trees to infer a species tree.
We also constructed relationships using SNPs. We constructed a phylogenetic network using splitstree (v4. 14. 6; Huson and Bryant 2006 ) and the full SNP dataset using the neighbour-net algorithm. We used the coalescent-based Bayesian phylogenetic approach SNAPP  (Bryant et al. 2012) in BEAST2 (v2. 5. 2; Bouckaert et al. 2019)  to infer relationships among different genetic clusters. We used the same SNP dataset as used with fastSTRUCTURE. We randomly chose 10 individuals per DAPC genetic cluster (or all individuals if a cluster had <10) as representatives. We ran the chain for 1 million generations, sampling every 1000 generations until effective sample size (ESS) values, assessed with Tracer v1. 7  (Rambaut et al. 2018) , were over 200 for all parameters. We performed two runs and combined these to ensure that they reached stationarity at the same point.
Phylogeographic Diffusion in Continuous Space We reconstructed the spatiotemporal dynamics of A. affinis using BEAST v1. 8. 4  (Drummond and Rambaut 2007 ) and spreaD3 v0. 9. 6  (Bielejec et al. 2011). As this analysis is computationally intensive we used a subset of our dataset by selecting only the five most informative loci based on number of phylogenetic informative sites. We added a partition consisting of longitude and latitude coordinates as continuous trait data. We used a HKY+G substitution model and a strict clock model for each genetic locus and an exponential growth coalescent tree prior. We ran the analysis for 100 million generations and assessed ESS values using Tracer v1. 7  (Rambaut et al. 2018). We then used spreaD3 to visualize the output at several time points during the history of A.