dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic structure within Taiwan In the context of Asian and Oceanian genetic variation, the genetic profile of THI/TOI is relatively homogenous and there is little structure revealed, except that they are separated from the Han groups, with the Lowland group falling in between them (Fig. 3A ). To further study the structure within Taiwan, we applied the haplotype-based method fineSTRUCTURE  (Lawson, et al. 2012). To provide haplotype sources from neighbouring groups, we carried out chromosome painting on all Austronesian, Tai-Kadai, and Sino-Tibetan groups in our dataset (Fig. 3B ), as these language families are proposed to be related  (Sagart 2008). The fineSTRUCTURE results generally support clustering according to language family except that groups from MSEA show a heterogenous pattern, which is consistent with previous studies suggesting complex histories involving extensive admixture and probable cases of language shift  (Liu, et al.
The central group Bunun falls toward Atayal, the Orchid Island group Tao falls toward Amis, and the lowland group Makatao is heterogeneous, with most falling with Han groups in the middle, but some lying toward Rukai/Paiwan. Notably, the Atayal and Amis from the published dataset fall together with the Atayal and Amis genotyped in this study. The fact that Atayal, Amis, and Rukai drive the poles of this PCA probably reflects their isolation and drift, as they show high amounts of withingroup IBD sharing (Fig. S5 ). Both ADMIXTURE and PCA results show that the Lowland group Makatao has an intermediate genetic profile between the THI/TOI and Han groups (Figs. 2  and 3A ), which suggests admixture. To test this, we computed the f3 admixture value f3(Minnan, Rukai; Makatao) and obtained Z= -7, indicating admixture in the Makatao involving these two proxy sources.
2018; Liu, et al. 2020; Kutanan, et al. 2021; Wang, Yeh, et al. 2021 ) and we kept only autosomal markers for our analyses. For quality control of the merged data of modern individuals, we excluded sites with more than 5% missing data in the entire dataset and sites with more than 50% missing data and/or Hardy-Weinberg equilibrium p values below 0. 00005 within a group (except for groups with only one individual). We removed individuals with more than 5% missing data and we excluded one individual from 1 st degree kinship pairs. Data missingness and Hardy-Weinberg equilibrium were calculated using PLINK v1. 9  (Purcell, et al. 2007 ) while the individuals to be removed to avoid 1st degree relatedness (kinship coefficient >= 0. 177) were inferred using KING  (Manichaikul, et al. 2010) , as implemented in PLINK v2  (Chang, et al.
2015). This gives a filtered dataset of modern individuals consisting of 540,697 SNPs and 1,664 individuals. Next, we further merged the filtered dataset with comparative ancient genomes  (Skoglund, et al. 2016; Yang, et al. 2017; Lipson, Cheronet, et al. 2018; Lipson, Skoglund, et al. 2018; McColl, et al. 2018; Posth, et al. 2018; Lipson, et al. 2020; Yang, et al. 2020; Pugach, et al. 2021; Wang, Yeh, et al. 2021; Wang, Wang, et al. 2021; Oliveira, et al. 2022 ). Six positions with more than two variants or that were inconsistent between the array and sequencing data were excluded. Ancient individuals with <15,000 informative sites were also removed. This gives a filtered dataset of modern and ancient individuals consisting of 540,691 SNPs and 1,958 individuals. For Principal Component Analysis (PCA), ADMIXTURE, and DyStruct analyses (described in below sections), variants were pruned beforehand for linkage disequilibrium using PLINK v1.
py. From K = 2 to K = 15, we performed 25 replicates for each K with random seeds. Then, we used the script plot_Q. py to plot the highest likelihood results for the best K of ADMIXTURE (K=9, with the lowest cross-validation error) and DyStruct (K=8, with the highest likelihood). Scripts bin_sample_times. py and plot_Q. py are provided by the authors of DyStruct (available here: https://github. com/tyjo/dystruct/tree/master/supp/scripts).
Data phasing Phasing was done by SHAPEIT version 4. 1. 3  (Delaneau, et al. 2019) , with East Asian (without the Kinh Vietnamese merged in our data set) and South Asian populations as a reference panel, and the recombination map from the 1000 Genomes Phase3 (Genomes  Project, et al. 2015). To prepare the reference panel, we extracted the East and South Asian individuals as well as the overlapping sites with our data for each chromosome from the 1000 Genomes Phase3 data using bcftools version 1. 4 (http://samtools. github. io/bcftools/; last accessed 10 July, 2020). The phasing accuracy of SHAPEIT4 can be enhanced by increasing the number of conditioning neighbors in the Positional Burrows-Wheeler Transform (PBWT) on which haplotype estimation is based  (Delaneau, et al. 2019). We ran phasing with the options -pbwt-depth 8 for 8 conditioning neighbors and left other parameters as default.
ChromoPainter, fineSTRUCTURE and GLOBETROTTER analyses ChromoPainter v2  (Lawson, et al. 2012 ) was run on the phased dataset focusing only on Austronesian, Tai-Kadai, and Sino-Tibetan speaking groups, with sample sizes for each population group randomly down-sampled to 7 (all individuals were used for groups with sample sizes below 7). We began with 10 iterations of the EM (expectation maximization) process to estimate the switch rate and global mutation probability using chromosomes 1, 5, 10, 15, and 20. With the estimated switch and global mutation rates, we ran the chromosomal painting process for all chromosomes, which then gave the output for downstream analyses. We first attempted to paint the chromosomes of each individual, using all of the samples in the dataset as both donors and recipients, via the -a argument.
The EM estimation of switch rate and global mutation probability were ~409. 33 and ~0. 00048, respectively, which were then used as the starting values for these parameters for all donors in the painting process. To specifically study the contribution of THI groups (Formosan branches) as sources for other Austronesians (Malayo-Polynesian branch), we also performed another run using all the samples except for non-THI Austronesians as both donors and recipients; non-THI Austronesians were used only as recipients. The EM estimation of switch rate and global mutation probability for this analysis were ~ 430. 08 and ~ 0. 00064, respectively. fineSTRUCTURE v4. 0. 1  (Lawson, et al. 2012 ) was run on the first ChromoPainter output using all of the samples in the dataset as both donors and recipients.
github. io/admixtools/). Finally, for each of the scenarios, we searched for the best-fitting admixture graph by running ten independent runs of find_graphs() and selected the one with the lowest score (computed through the residuals between the expected and observed f-statistics given the data). We further confirmed a fitting graph by testing the graph with the lowest score using qpgraph() to check if the absolute value of the worst-fitting Z score is below 3. We started with no migrations (numadmix=0) and then added migrations till we found a fitting graph, which we denoted as the best-fitting graph for that scenario. We also added different admixture constraints for searching the graphs depending on our questions; for the admixture profile of Makatao, at least 1 admixture for Makatao, and no admixtures for Atayal, Rukai, and Amis; for Into-Taiwan events, no admixtures for ~8.
Acknowledgements We thank all sample donors for making this work possible. We dedicate this paper to  Prof. Ying-Chin Ko , a pioneer in the public health research of Taiwan aborigines and a tireless worker on behalf of Taiwan's aboriginal communities. We thank  Benjamin Peter ,  Stephan Schiffels ,  Sandra Oliveira ,  Irina Pugach ,  Hsiao-Chun Hung ,  Stacy Fang-Ching Teng  and  Etienne Patin  for helpful discussion concerning the interpretation of results. This study was supported by the  Max Planck Society.
This figure reports survey estimates of youth (18 to 34) and adult (35+) turnout from the most recent election for which sufficiently high-quality data are available. Democracies are those countries that have a Polity-V score above 5. See section S1 for details about sources and construction. (22) , are "conditional voters" who will choose to participate under certain conditions. Accordingly, in our exploratory subgroup analysis, we divided study participants into three subgroups: unconditional voters, who are committed to voting irrespective of their exposure to any additional messaging before elections, conditional voters, who do not turn out to vote in the status quo, and (unconditional) nonvoters, who will not vote even when exposed to any of the three treatments. This is because, for this group, relatively light-touch interventions cannot sufficiently increase d or p*, nor decrease c to push u i above zero.
Design and data We designed our study around Morocco's local and general elections, which were held jointly on 8 September 2021. We conducted an online panel study around the election with the baseline survey conducted before the election and a follow-up survey conducted immediately after the election. To maximize the sample size, we used three rounds of recruitment spread between late June and early August 2021. For the sake of external validity and scalability of our results, we opted not to use a panel survey firm, and instead used Facebook ads to recruit participants. A total of 7521 participants completed the pre-election survey. Participation in the study was restricted to Moroccan citizens who were between 18 and 35 years old and currently resided in Morocco. Moroccan residency was confirmed by sending a confirmation text message to Moroccan-registered phone numbers.
Acknowledgments: We thank  C. Weiss  and attendees at the 2022 American Political Science Association Annual Meeting for their feedback. We thank  Tafra  and its director  Z. Haddaoui  for their implementation support. Funding: We acknowledge financial support from the  University of Pennsylvania's Christopher H. Browne Center for International Politics  and from the  School of Arts and Sciences' Global Inquiries grant initiative, New York University Abu Dhabi Postdoctoral Research Program in Social Sciences , the  International Foundation of Electoral Systems, and Tafra. H. L. acknowledges funding from the  French Agence Nationale de la Recherche  under the  Investissement d'Avenir program   ANR-17-EURE-0010. R. F. acknowledges funding from the  S C I E N C E A D VA N C E S | R E S E A R C H A R T I C L E Ferrali et al.
We here test whether the ensemble size of MPI-GE CMIP6 is sufficiently large to be used for infilling the HadCRUT5 data set with similar capability than the 100-member MPI-GE CMIP5. The models used to infill the HadCRUT5 data set  (Morice et al. , 2021)   (Kadow et al. , 2020)  to improve the computational performance and the quality of the reconstruction. In particular, a custom padding operation accounting for the boundary conditions of the global data is now applied before each partial convolution, to account for the sphere of the Earth. We compare the temperature climatologies of 1991-2020 and 1891-1920 of both the original HadCRUT5 data (Figure  7a ) and the HadCRUT5 analysis (Figure  7b ) with the artificial intelligence-infilled versions using the 100-member MPI-GE CMIP5 (Figure  7c ) and the 30-member MPI-GE CMIP6 (Figure  7d ).
This comparison shows the substantial difference in the availability of information between the HadCRUT5 data sets and the artificial intelligence-infilled versions. Even the HadCRUT5 analysis, infilled using a method related to Kriging, does not cover the whole globe and provides a rather smoothed estimate of temperature change. With the artificial-intelligence models, we detect both strong warming and slight cooling in the regions where the HadCRUT5 data sets have missing data. In particular, the warming patterns reconstructed from the two ensembles show a strong warming signal over the century in northern high latitudes. Large areas in the Pacific also consistently show a warming between the two climatologies, despite the fact that the region is affected by strong El Niño-Southern Oscillation variability.
The artificial intelligence-reconstructed data in the sparsely observed Southern Ocean and Antarctica reveal pronounced regional differences of warming and cooling. The annual global mean temperature time series reconstructed using the 100-member and the 30-member artificial-intelligence models are very similar to each other, but different when compared to the original HadCRUT5 data and its analysis (Figure  S6  in Supporting Information S1). The early years of the artificial intelligence-infilled observations show in general a cooler temperature anomaly than the original HadCRUT5 data  (Kadow et al. , 2020). From the striking similarity in the reconstructed pattern and time series between MPI-GE CMIP5 and MPI-GE CMIP6, we conclude that MPI-GE CMIP6 allowed us to train a model with equivalent capabilities to MPI-GE CMIP5 but at a lower computational cost.
We applied this framework to metagenome-based Protein Functional Clusters (PFCs; hereafter referred to as "clusters") linked to RUBISCO and C4enzymes only, in marine picoeukaryotes. Compared to a more traditional approach (i. e. , searching reads in a functional database using sequence similarity), our methodology combining MAGs and PFCs offers several advantages. The quantitative signal resulting from a MAG is (i) standardized by the genome length and (ii) correspond to a taxonomic identity. Combined in PFCs, (iii) it also includes the fraction of signal corresponding to not yet annotated genes. Thus, this approach offers a more robust quantitative framework than traditional approaches, representative of eukaryotic plankton diversity in open oceans (39. 1 billion reads recruited, ~97% identity, ~25 Gbp; , 28) and transferable to a variety of functions or enzymes of interest using the already computed PFC network.
C4-CCM enzymes across sampled stations From the Tara Oceans eukaryotic MAGs, ~1. 2 million clusters were built, for which 349 are related to RUBISCO or C4-enzymes (Fig. S1 , Table  S1 ). This dataset corresponds to 817 unique genes, with a median observed presence across 45 sampled stations per cluster. To avoid considering enzymes related to other metabolic functions, we only selected those related to RUBISCO or C4enzymes only, corresponding to 240 clusters, distributed across the world Ocean except the Arctic, western Pacific and to a lesser extent Southern Ocean (Figure  1 ). The successive cluster selection criteria (i. e. , PFCs exclusive to RUBISCO or C4-enzymes, minimum presence at 10 sampling stations) did not present significant effects on the distribution of clusters across number of reads, number of genes and taxonomic classes (Fig.
Genomic data We studied the biogeography of the genomic potential related to C4-CCM through the prism of Metagenomic Assembled Genome (MAG, 28) retrieved from the Tara Oceans expedition  (2009) (2010) (2011) (2012) (2013). Briefly, 280 billion reads from 798 metagenomes, corresponding to the surface and deep chlorophyll maximum layer of 210 stations from the Pacific, Atlantic, Indian, Southern and Arctic Oceans, as well as the Mediterranean and Red Seas (Figure  1 ), encompassing eukaryoteenriched plankton size fractions ranging from 0. 8 µm to 2 mm, were used as inputs for 11 metagenomic co-assemblies (6-38 billion reads per co-assembly) using geographically bounded samples. We thus created a culture-independent, non-redundant (average nucleotide identity <98%) genomic database for eukaryotic plankton in the sunlit ocean consisting of 683 MAGs and 30 single-cell genomes (SAGs), all containing more than 10 million nucleotides for a total size of 25.
Protein functional cluster selection We first selected a reduced set of clusters, within the 0. 8 to 5 µm size fraction and surface samples, for which 100% of the KEGG Orthology (KO, 50) annotated protein members were related to C4enzymes or RUBISCO (Fig. S1 , Table  S1 ). To avoid model over-parameterization and because rare clusters were assumed as not influencing the large-scale patterns investigated in this study, we only considered clusters that were present in a minimum of 10 Tara Oceans stations. The corresponding dataset contained 240 clusters distributed across 130 Tara Oceans stations. The 240 clusters, functionally annotated with C4-enzymes and RUBISCO, were associated with 234 MAGs. The latter presented an average completeness estimate of 57% (Table  S3 ). In comparison, the average completeness estimate across all MAGs from Delmont et al.
(28)  yield at 37 %. As a supplementary quality check, we estimated a minimum horizontal coverage (i. e. , number of bases of a MAG covered with a certain depth) of 68 % for each of the 234 MAGs (Table  S3 ). Finally, we show that our MAGs are associated with an average BUSCO completeness (i. e. , the percentage of mapped BUSCO genes in each MAG) of 55. 7% (Table  S3 ). We therefore consider these MAGs of sufficient quality for identifying C4-genes across our samples. To reduce the number of response variables (clusters; PFCs) to a reasonable amount for multivariate modelling, with respect to the limited number of stations, we performed an Escoufier dimensional reduction  (51). The latter iteratively selects the clusters whose pattern across stations minimize the residual variance of the dataset.
Metagenomic data pre-processing Genes abundances among samples were determined by mapping raw metagenomic reads against the gene database (28). Briefly, reads were mapped using the bwa tool, and only random best matches with at least 95% of sequence identity over at least 80% of the read length were retained as positive. To alleviate the effect of gene length and sequencing effort variability between samples on the number of reads, we normalized the metagenomic reads by the length of the corresponding gene coding part and the total number of reads per station (i. e. , including reads of all nonconsidered clusters), respectively. Because the total genomic material present at each sampling station is unknown (i. e. , non-exhaustive sampling and sequencing effort), the absolute number of reads is not comparable among stations.
Model training and evaluation To avoid over-fitting, the explanatory and response datasets were split between training set and test set using a n-fold cross-validation procedure. For each model, n algorithms were trained on different n-1 folds, while the remaining fold was used for testing only (i. e. , computing the loss at each boosting round). To minimize the effect of spatial and temporal autocorrelation in our data (i. e. , leading to over-optimistic model evaluation, 52), the n-folds were defined according to the Tara Oceans station number. Because the cruise followed a continuous trajectory in time and along the sampled stations, the resulting folds are spatially and temporally distant (i. e;, spatial and temporal block splitting, as recommended in 52). The resulting n-algorithms predictions were aggregated in an average response and its corresponding coefficient of variation (CV).
Spatial projections To better estimate projection uncertainty, our spatial projections were constructed using a bootstrap procedure. For each 100-bootstrap round, we first re-sampled the original dataset (i. e. , train and test response dataset and corresponding explanatory variable values) with replacement. Then, we re-fitted an MBTR algorithm on the re-sampled data by using the hyperparameters corresponding to the validated model, including the number of boosting rounds corresponding to the minimum loss across all n-algorithms. Finally, the re-fitted MBTR algorithm was used to predict the relative abundance of clusters worldwide, using the corresponding climatologies values at each geographical cell.
From model projections to final outputs We only modelled the 50 clusters representing 95% of the dataset variability. Therefore, we indirectly reconstructed the projections of the 190 others by identifying their most representative Escoufier-selected cluster. To this extent, we performed a correspondence analysis based on the observed relative abundance of all clusters. By using the dimensions of the correspondence analysis space corresponding to a minimum of 80% variance explained, we calculated the Euclidean distance between each non-selected cluster, and its nearest neighbor selected by the Escoufier criteria. Because the 50 Escoufier selected clusters represented over 95% of the dataset variability, we considered that a cluster and its nearest neighbor in the correspondence analysis space share the same relative abundance pattern.
Data availability Instructions on how to build the Sequence Similarity Network and associated Protein Functional Clusters are available at: https://data. d4science. net/BN9t. Instructions and credentials on how to access the genomic database used in this study (PostgreSQL) are available in the technical documentation at https://data. d4science. net/qa7Z. The ensemble of enzyme-level projections are available upon registration at [https://data. d4science. net/Zraq]. Additional data that support the findings of this study are available from the corresponding author upon reasonable request.
The likelihood of this model is well-known  (Nee et al. , 1994)  and depends only on the phylogeny's branching times and not on its topology. To estimate diversification rates with the MLE, e used the APE R package  (Paradis et al. 2004 ). In the Binary State Speciation and Extinction (BiSSE) model, lineages can alternate between two states (0 and 1), where each state has its own speciation and extinction rate. The switch through time between the two states translates character transitions (e. g. sexual to asexual reproduction) that can have an impact on diversification rates. The model has 6 parameters: 2 speciation rates (λ 0 , λ 1 ), 2 extinction rates (μ 0 , μ 1 ) and 2 two transition rates (q 01 for transition 0 → 1; q 10 for transition 1 →0). As for the CRBD model, the likelihood of the BiSSE model can be computed (see  Maddison et al.
DEEP LEARNING TO INFER PHYLOGENY PARAMETERS The constraints are: 1) λ 1 = 2λ 0 ; 2) μ 0 = 0; 3) μ 1 = 0; 4) q 01 = q 10. Constraint 1) ensures that states 0 and 1 have different speciation rates, 2) and 3) make the model pure birth, and 4) is an assumption of symmetry that makes the probabilities to switch from one state to another equal. Lambert et al. (2022)  also used the CRBD model, and a less constrained version of BiSSE. Here we simplified the model to focus on the comparison of network architectures and data representation. To estimate diversification rates with the MLE, we used the Diversitree R package (FitzJohn 2012) and constrained the likelihood with the 4 constraints listed above. To train the neural networks, we simulated 100,000 phylogenies for the CRBD model and 1,000,000 phylogenies for the BiSSE model using the Diversitree library (FitzJohn 2012) in R.
-The main motivation for the previous CBLV encoding is to transform the phylogeny into a regular Euclidean format that can be easily processed by CNNs. The disadvantage, however, is that neighborhood relationships of the graph can be distorted in this process. Recent research in the field of machine learning suggests that in such a case, it is often better to train neural networks directly with the original graphs. This is done by so-called Graph Neural Networks or Ismaël Lajaaiti, Sophia Lambert, Jakub Voznica, Hélène Morlon, Florian Hartig GNNs, which extend the CNN idea to graphs. Here, we use the Pytorch Geometric framework  (Fey and Lenssen 2019)  and the GraphNeuralNetworks Julia package base on the Flux framework  (Innes 2018)  to represent the phylogeny as graph and train GNNs. We provide the GNN with the phylogeny's topology and 4 attributes per node: distance to the root and the lengths of the 3 edges linked to the node (1 incoming edge: parent → node; 2 outcoming edges: node →child 1 and node → child 2 ).