dataesr/scientific-paragraphs-categorization huggingface dataset:
Only proteins with at least two peptides were retained in the present study. In this condition, the false discovery rate (FDR) assessed with the corresponding reversed decoy database search was less than 1% at the protein identification level. Peptide-to-spectrum matches (PSMs) were counted for each protein for label-free quantification (spectral counting). For each protein, the normalized spectral abundance factor (NSAF) was calculated as the sum of its spectral counts observed in all the samples, divided by its molecular mass expressed in kDa  [52]. The NSAF values were then presented as a ratio (%NSAF) by dividing the NSAF of each protein to the sum of all NSAF values detected for the whole dataset. The "Strict core proteome" was defined as all the proteins systematically identified in all the samples, while the "Core proteome" included proteins identified in at least one replicate of each experimental condition.
The "Pan proteome" consists of all the proteins identified in this study. The annotation of proteins through gene ontology (GO) terms was performed using a DIAMOND search against the UniRef50 database and the homologs found in this search were mapped to GO terms through the GO annotation database of the reference genome, C. canephora, downloaded from Coffee Genome Hub (http://coffee-genome. org/download, accessed on 1 February 2021). GOslim terms were obtained by using the Map2Slim option in OWLTools and the generic GOslim list of GO entries.
Main Proteome Features of Coffea canephora and Coffea arabica Regardless of Water Availability Label-free quantitative shotgun proteomic analysis based on 18 plants from the two coffee genotypes allowed to obtain a dataset with 1. 4 million MS/MS spectra, of which a total of 520,806 could be assigned to peptide sequences from the protein database. This resulted in a 36% detection rate, with a total of 23,871 peptide sequences found within the whole dataset. A total of 3425 polypeptide sequences were identified and quantified. Spectral counts are indicated in Table  S1. Taking together all water availability treatments, 2982 polypeptides were found in CL153 and 3046 polypeptides in Icatu, from which 2752 proteins (ca. 90% of proteins per genotype) were commonly identified in the two genotypes.
We also reconstructed a phylogenetic tree using nuclear ultraconserved elements (UCEs) derived from a subset of 18 Kalotermitidae species. Using these comprehensive phylogenies, we reconstructed the global historical biogeography of Kalotermitidae,  Buček et al. • https://doi. org/10. 1093/molbev/msac093 MBE identifying past natural and human-mediated dispersal events and distribution patterns that bear the signature of potential events of vicariance through continental drift. Finally, we created a list of species of Kalotermitidae with available foraging data and discussed the evolution of such abilities according to patterns implied by the phylogenetic framework.
Results and Discussion Evolution of Kalotermitidae Inferred from Taxonomically Representative Phylogenetic Trees We gathered two sequence datasets. The first dataset comprised mitochondrial genomes of 172 samples of Kalotermitidae collected across all biogeographic realms (fig 1, supplementary fig. S1  and table  S1 , Supplementary Material online). The second dataset included 82-691 UCE loci for a representative subset of 18 species belonging to 14 kalotermitid genera. We reconstructed maximum likelihood and Bayesian time-calibrated trees using these datasets combined with nonkalotermitid outgroups (see Materials and Methods and supplementary table  S2 , Supplementary Material online for details). The topologies and branch lengths of maximum likelihood trees based on 82 and 691 UCEs were highly congruent (supplementary fig.
S2 , Supplementary Material online). For this reason, and because of computational power limitation, we used 82 UCEs to reconstruct Bayesian time-calibrated trees. To determine the role of sampling effort and sequence type on the variation of node age estimates, we compared phylogenetic trees reconstructed with 230 mitochondrial genomes (including 172 samples of Kalotermitidae), 28 mitochondrial genomes, and 28 UCEs (including 18 samples of Kalotermitidae). We found that the time-calibrated trees inferred with the UCE and mitochondrial data of 28 species were largely congruent in terms of topology and node age estimates, indicating that our phylogenetic inferences were not influenced by the type of genetic markers used (fig. 2 , supplementary fig. S3 , Supplementary Material online). The topology of the tree inferred with 230 mitochondrial genomes was also highly congruent with the trees inferred with the UCEs and mitochondrial genomes of 28 species.
However, the trees reconstructed with 230 mitochondrial genomes yielded mean age estimates up to 20 Ma older than those of the trees reconstructed with data from 28 species, although their 95% HPD intervals overlapped for most nodes (fig. 2 ). Next, we investigated the effect of the substitution model by comparing time-calibrated trees reconstructed from the dataset composed of 230 mitochondrial genomes. The analyses performed with a nucleotide substitution model for all mitochondrial genes were highly congruent in terms of topology with the analyses performed with an amino acid substitution model for protein-coding genes and a nucleotide substitution model for rRNA and tRNA genes (fig. 2 ). Both analyses yielded time-calibrated trees with overlapping 95% HPD intervals for all nodes outside some of the youngest nodes.
Therefore, the sampling effort had more influence on age estimates than the choice of genetic markers or evolution models, and we used the most comprehensive dataset composed of 230 mitochondrial genomes to reconstruct the natural history of Kalotermitidae. We performed multiple maximum likelihood and Bayesian phylogenetic reconstructions using combinations of protein and nucleotide substitution models, including or excluding the third codon positions (supplementary table  S2 , Supplementary Material online). The node support statistics of all trees are summarized in a reduced summary evidence tree (RSE-tree) (fig. 1 ). The RSE-tree was obtained by pruning 35 tips that diverged ,1 Ma and were represented by samples collected in the same country. Our RSE-tree was composed of 137 tips representing 21 of the 23 genera of Kalotermitidae and including more than 120 species, which is equivalent to 27% of described kalotermitid species-level diversity  (Krishna et al.
This age estimate was corroborated by a Bayesian tree inferred with the UCE dataset that yielded 119 Ma (96-145 Ma 95% HPD) as an age estimate for the divergence between Kalotermitidae and Neoisoptera (supplementary fig. S3 , Supplementary Material online). The differences with previous studies may result from using different fossil calibrations among studies, changes of fossil age estimates based on revised or refined stratigraphic and radiometric dating for fossil deposits, or different taxonomic sampling among studies. Regardless, all studies estimated the origin of Kalotermitidae to predate the first kalotermitid fossils, the 99-million-year-old species of Proelectrotermes  (Engel et al. 2007b) , by 6-28 million years. Crown-group Kalotermitidae share a common ancestor estimated to live 84 Ma (75-93 Ma 95% HPD, node 4 in fig.
Reconciliation of Extant and Extinct Kalotermitid Evidence Molecular phylogenies are reconstructed using extant taxa or, rarely, using well-preserved fossils younger than 1 Ma  (van der Valk et al. 2021). The exclusion of extinct species can lead to spurious historical biogeographic reconstructions  (Lieberman 2002). Although fossil evidence has been integrated into biogeographic reconstructions inferred from molecular phylogenies by previous studies (e. g. ,  Meseguer et al. 2015) , these methods are hampered by an incomplete fossil record and uncertain taxonomic affiliations, as is the case of Kalotermitidae. Kalotermitids are represented by 74 fossils belonging to 12 extinct and six extant genera in the paleontological database PaleoDB (supplementary fig. S4 , Supplementary Material online).
MBE We obtained an average of 52 megabases and 2 gigabases of raw sequencing data for samples sequenced with the amplicon-based approach and samples sequenced with the shotgun sequencing approach, respectively. Raw sequencing data was quality-checked with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and trimmed and quality-filtered with bbduk (sourceforge. net/projects/bbmap/) using the following parameters: "ktrim = r k = 23 mink = 7 hdist = 1 tpe tbo maq = 10 qtrim = rl trimq = 15 minlength = 35. " Multiple mitochondrial genome assembly approaches were used as described below, and the longest mitochondrial genome contigs or scaffolds were selected for each sample. The mitochondrial genomes obtained through long-range PCR amplifications were assembled using SPAdes with default parameters  (Bankevich et al.
2012 ). The mitochondrial genomes obtained from shotgun sequencing data were assembled with one of the three following methods: (1) metaSPAdes with default parameters  (Nurk et al. 2017)  ; (2) metaSPAdes with default parameters followed by the iterative mapping-reassembling approach implemented in TCSF and IMRA  (Kinjo et al. 2015)  and using the mitochondrial genome of C. brevis as a reference; (3) Novoplasty organelle genome  (Dierckxsens et al. 2016 ) with default parameters and using C. brevis mitochondrial genome as a seed sequence. The mitochondrial genomes assembled in multiple high-coverage contigs were merged using published kalotermitid mitochondrial genomes as references. Assembly gaps were filled with the symbol N (supplementary table  S2 , Supplementary Material online). All mitochondrial genomes were cyclically shifted using MARS  (Ayad and Pissis 2017)  to start with the tRNA gene coding for isoleucine and end with the 12S rRNA gene.
The resulting trimmed mitochondrial genomes were annotated with MitoZ  (Meng et al. 2019). The genome annotations of previously published mitochondrial genomes were retrieved from GenBank. MitoZ annotations were visually inspected by first aligning mitochondrial genomes with MAFFT as described above. The alignments and the MitoZ-generated feature annotations were inspected with JalView  (Clamp et al. 2004 ). The errors in MitoZ-generated annotations, mostly comprising of omitted tRNA-Val and 5 ′ -terminally truncated 16S rRNA gene, were corrected manually. The final mitochondrial genomes and their annotations are available on GenBank (supplementary table S1, Supplementary Material online). The final mitochondrial genome sequence dataset consisted of 164 mitochondrial genomes. We also used previously published sequences, including the mitochondrial genomes of eight species of Kalotermitidae  (Cameron et al.
2012; Bourguignon et al. 2015 ) (supplementary table  S2 , Supplementary Material online) and 58 mitochondrial genomes of nonkalotermitid termites that we used as outgroups  (Bourguignon et al. 2015). The mitochondrial genome sequence dataset used for comparison with the UCE dataset included an additional ten publicly available nonkalotermitid termite mitochondrial sequences (supplementary table  S4 , Supplementary Material online). The rRNA and tRNA sequences were aligned using MAFFT v. 7. 475  (Katoh and Standley 2013). For proteincoding genes, we first translated the nucleotide sequences into amino acid sequences with the transeq command of the EMBOSS suite of programs  (Rice et al. 2000)  using the invertebrate mitochondrial genetic code. The protein sequences were then aligned using MAFFT v.
7. 475. The protein sequences were either directly concatenated with the noncoding nucleotide sequences or were first backtranslated into nucleotide sequence alignments using PAL2NAL v. 14  (Suyama et al. 2006 ) before concatenation with the noncoding nucleotide sequences. All alignments were concatenated with FASconCAT v. 1. 04 (Kück and Meusemann 2010). We also generated alternative alignments, one with the third codon sites of protein-coding genes and one without the third codon sites of proteincoding genes. An additional mitochondrial genome dataset included the COII sequence of Ceratokalotermes spoliator  (Thompson et al. 2000)  to elucidate its phylogenetic position.
UCE Datasets UCEs were extracted from metaSPAdes assemblies using the PHYLUCE suite of programs  (Faircloth 2016)  with the baits and parameters used by  Hellemans et al. (2021). Since the coverage of the Kalotermitidae wholegenome shotgun sequence data was not uniform among samples, we selected 18 Kalotermitidae species as a tradeoff between maximizing the number of species and genera and maximizing the completeness of the UCE matrices. The final UCE datasets included 28 species (18 Kalotermitidae species and 10 non-Kalotermitidae termite species; see supplementary table S4, Supplementary Material online). All extracted UCEs were deposited in Dryad (https://doi. org/10. 5061/dryad. 5mkkwh77v; https:// github. com/oist/TER-UCE-DB/). Two UCE datasets were Past Transoceanic Voyages of Drywood Termites • https://doi.
org/10. 1093/molbev/msac093 MBE used for phylogenetic analyses. The first dataset had a completeness of 70% and included 691 loci, whereas the second dataset was complete at 75% and included 82 loci. Alignments were produced with MAFFT and trimmed using Gblocks  (Castresana 2000) , as implemented in the PHYLUCE suite of programs.
Maximum Likelihood Phylogeny Sequence alignments were used to infer maximum likelihood phylogenies with IQtree v. 1. 6. 7  (Nguyen et al. 2015). IQtree was run with the extended nucleotide model selection, including FreeRate models. The best partitioning scheme was determined from a set of a priori defined partitions using ModelFinder  (Kalyaanamoorthy et al. 2017)  implemented in IQtree (option "-m MFP + MERGE"). The partition scheme searching was constrained by setting the maximum number of partition pairs during the partition merging phase to 2,000 and considering only the top 10% partition schemes (options "rcluster-max 2000 -rcluster 10 -bnni"). The partitions of the mitochondrial genome datasets were defined a priori assuming differences in evolutionary rates among codon positions of protein-coding genes, tRNAs, and rRNAs.
Therefore, we set one partition for each codon position of the 13 mitochondrial protein-coding genes, one partition for the 22 tRNA genes, and one partition for the 12S and 16S rRNA genes. For UCE datasets, each genetic locus was predefined as a separate partition. Branch supports were calculated using ultrafast bootstrapping method (option "-bb 1000")  (Minh et al. 2013). The reconstructed trees were rooted using Mastotermes darwiniensis, the extant sister lineage of all other termites  (Kambhampati et al. 1996). See supplementary table  S2 , Supplementary Material online for an overview of maximum likelihood analyses.
Bayesian Time-Calibrated Phylogeny We estimated time-calibrated phylogenies of Kalotermitidae using the Bayesian phylogenetic software BEAST2 v. 2. 6. 2  (Bouckaert et al. 2014). We used the uncorrelated lognormal relaxed clock model to account for substitution rate variation among branches  (Drummond et al. 2006). Several alternative datasets were used: (1) a UCE dataset including 28 species, (2) a mitochondrial genome dataset including 28 species and consisting of protein sequence alignments for protein-coding genes and nucleotide sequence alignments for rRNA and tRNA genes, (3) a mitochondrial genome dataset including 230 species and consisting of protein sequence alignments for proteincoding sequences and nucleotide sequence alignments for rRNAs and tRNAs, and (4) a mitochondrial genome dataset including 230 species and consisting of concatenated nucleotide sequence alignments for all genes.
The MCMC chains were run three times independently for each dataset. We used between 169 × 10 6 and 337 × 10 6 generations sampled every 10,000 generations to build the final maximum clade credibility trees. The convergence of the MCMC chains was inspected with Tracer 1. 7. 1  (Rambaut et al. 2018). Between 10% and 30% of initial generations were discarded for each MCMC chain following visual inspection of trace plots that ensured MCMC chain convergence. The trees sampled from the MCMC runs were summarized as maximum clade credibility tree with TreeAnnotator v2. 6. 2  (Bouckaert et al. 2014).
Fossil Calibrations We used 15 termite fossils as minimal age internal calibrations (supplementary table  S5 , Supplementary Material online). In all cases, we used the youngest fossil age estimates reported on the Paleobiology Database (https:// paleobiodb. org/, accessed November 2021). Note that fossil age estimates can be controversial and alternative age estimates exist (e. g. ,  Kasinski et al. 2020  for hypotheses on Baltic amber age). However, the widths of the 95% HPD intervals produced by our Bayesian time-calibrated phylogenies typically exceed the interval of alternative estimates of fossil ages. Therefore, we presume that our inferences of time-calibrated phylogenies are robust with respect to the use of alternative fossil age estimates. For reproducibility, we adhered here to the ages reported in Paleobiology Database although these might not always reflect the latest opinions on the fossil age.
Lastly, Kalotermes piacentinii (PaleoDB collection number 120351), originally described as a termite fossil  (Piton and Théobald 1937) , is no longer considered to be a termite fossil  (Nel and Paicheler 1993). See supplementary table S6, Supplementary Material online for an overview of the Kalotermitidae fossils retrieved from the Paleobiology Database.
Spectroscopic data The spectroscopic data employed in this study were drawn from a variety of different surveys. For all redshift surveys we selected only those galaxies with secure z spec measurements, the definition of which differs slightly based on the survey being used, and limited the redshift range to 2 < z spec < 5. The majority of our z spec values (53. 7%) were taken from VUDS. This survey primarily utilizes photometric redshifts (hereafter z phot ) drawn from the photometric catalogs described above  4  to select targets for spectroscopic follow up using VIMOS  (Le Fèvre et al. 2003)  mounted on the Nasmyth platform of the 8. 2-m Very Large Telescope (VLT) Unit Telescope 2 (UT2/Melipal) at Cerro Paranal. The spectroscopic targets are largely limited to i /i + /I < 25 and those objects whose primary or secondary z phot solution satisfies z phot + 1σ > 2.
The dependence of physical parameter offsets on sample variance As discussed in Sect. 2. 3, the median values of M * and SFR estimated for galaxies in the three fields studied in this paper varied. Specifically, for our full spectroscopic sample, there was an offset of +0. 03/+0. 07 dex and -0. 13/+0. 14 dex for M * and SFR, respectively, for galaxies in the ECDFS and CFHTLS-D1 field, respectively, relative to those in the COSMOS field. In this section, we investigate whether these differences may be due to sample variance effects. The coverage of our spectral data is the largest in the COS-MOS field out of the three fields studied here, with the coverage being roughly two and five times the size of the CFHTLS-D1 and ECDFS fields, respectively. Since all photometric data in an individual field is treated in a homogenized manner, we can test the ability of sample variance alone to reproduce the offsets seen in the M * and SFR values for the average galaxy in the CFHTLS-D1 and ECDFS fields by sub-sampling the COSMOS data in regions that are of equivalent sizes to these two fields.
However, the median offsets from field to field in both parameters are nearly identical to those of the full VUDS+ sample. We conclude from these exercises that the observed offsets are unlikely due to sample variance or selection effects, and are rather primarily an artifact of the heterogeneous imaging observations and treatment of the photometric data across the various fields. None of the results presented in this paper change meaningfully whether or not the observed offsets in M * and SFR are applied to the galaxies in the non-COSMOS fields.
Acknowledgements. This paper is dedicated to  Dr. Olivier Le Fèvre. Though he was unable to witness its completion, it was his vision and tenacity that made this work, among countless others, possible. Based on data obtained with the  European Southern Observatory Very Large Telescope, Paranal, Chile , under Large Programs  175. A-0839 ,  177. A-0837 , and  185. A-0791. Some of the material presented in this paper is based upon work supported by the  National Science Foundation  under Grant No. 1908422. This work was additionally supported by the  France-Berkeley Fund , a joint venture between  UC Berkeley, UC Davis , and le  Centre National de la Recherche Scientifique de France  promoting lasting institutional and intellectual cooperation between France and the  United States. This work was supported by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY  and by  INAF  Grants  PRIN 2010 ,  PRIN 2012  and PICS 2013.
The land use/land cover (LULC) map For the purpose of our simulations and analyses, we produced a LULC map of the study area by applying a series of basic GIS operations to three different spatial datasets available in vector format: 1) the RPG (Registre Parcellaire Graphique) from the Common Agricultural Policy (CAP) declarations (<www. data. gouv. fr>), 2) the BDTOPO (2010) nationwide database, produced by the French national mapping agency (IGN) (<https://geoservices. ign. fr/documentation/diffusion/telechargement-donnees-libres. html>), 3) the OCS-GE (OCcupation du Sol à Grande Échelle) database also obtained from IGN. The three datasets were topologically corrected and combined in a single vector layer to produce a map with the highest possible accuracy. The resulting map was composed of 92 LULC categories with 81 agricultural land use categories (e.
We decided to study bee species nesting in belowground nests since the majority of wild bee species are ground nesters  (Cane 1991 , Michener 2007). For each LULC category, the availability of belowground nests was attributed using expert knowledge. Concerning the availability of floral resources, we defined 12 indices associated with the 12 months of the year. For 70% of the LULC categories, the values of the floral resource indices were based on information reported in existing databases containing records of pollen and nectar production of plant taxa (mainly EFSA 2013,  Baude et al. 2016). For the remaining LULC categories (30%) the floral resource indices were attributed using expert knowledge (see the Supporting information for details). Concerning the table of pollinator attributes, we defined 16 modelled bee categories that only differed in their average foraging distance (α-index).
We randomly selected fields between 10 000 m 2 and 20 000 m 2 in size, located at least 5 km from the border of the study area, and separated from each other by at least 4 km (Fig. 3 ). To assess the distanceweighted effects of landscape composition on the visitation rate of modelled bees, i. e. how the landscape effects decline with increasing distance from the sampling sites, we cropped the LULC input map around the centres of the 50 sunflower fields according to different buffer radii. For each sunflower field, 108 circular extents were defined with a buffer radius ranging from 10 to 90 m (10-m increment) and from 100 to 5000 m (50-m increment). We ran the InVEST pollination model using Python ver. 2. 7. 16 programming language iteratively with the 5400 circular input maps (50 fields × 108 buffer radii) and for the 16 modelled bees (average foraging distance ranging from 50 to 800 m).