dataesr/scientific-paragraphs-categorization huggingface dataset:
Analogously to genetic differentiation patterns, we find a positive correlation between differences in utterance frequencies and actual birth-places' distances (Spearman ρ=0. 2794, two-sided Mantel p<2x10 -4 ), as well as paternal and maternal birth-places respectively (Table  T1 ). However, contrarily to ASD, we find that utterance-frequencies differences stem from inter-birth-islands' distances, rather than shorter distances within islands only. Consistently with previous results from Santiago only  (21) , these results altogether show that speech-varieties are significantly transmitted from one generation to the next throughout Cabo Verde, anchored in individuals' birth-places, albeit not accounting for the majority of observed linguistic variation in our dataset. Indeed, we find that age-differences also substantially correlate with utterance-frequency differences even correcting for individual birth-places (Spearman ρ=0.
MetHis-ABC prior checking We considered four competing genetic-admixture scenarios (Figure  F6 ; Table  T2 ), tested separately for individuals born on each Cabo Verdean island with MetHis-ABC machine-learning scenario-choice and posterior parameter inferences  (37, 53, 54). ABC inferences are based on 42 summary statistics (Table  ST3 ) calculated from 60,000 independent autosomal SNPs in individuals form each Cabo Verdean island, and in the African Mandinka and European Iberian-IBS proxy source populations. We find that the summary-statistics calculated from the observed datasets fall well within the space of summary-statistics obtained from 10,000 simulated-datasets under each of the four competing scenarios (Fig-  For all scenarios, the duration of the admixture process is set to 20 generations after the initial founding admixture event occurring at generation 0, which corresponds roughly to the initial peopling of Cabo Verde in the 1460's, considering 25 years per generation and sampled individuals born on average between the 1960s and 1980's.
Other Western, Central, and South-Western African populations historically forcibly deported during the TAST seem to have had only very limited contributions to the genomic diversity of Cabo Verde, and only in certain islands. This could be due to Cabo Verde being only a temporarily waypoint for these latter enslaved-African populations between Africa, the Americas, and Europe, but would also be consistent with additional socio-historical processes (see below). Interestingly, and further echoing these genetic results, the Cabo Verdean Kriolu language carries specific signatures mainly from the Mande language-family, and Wolof and Temne languages from Western Africa, and largely more limited signatures of Kikongo and Kimbundu Bantu languages from Central and South-Western Africa  (15, 28). These results contrast with the admixture patterns identified in other enslaved-African descendant populations in the Americas in our dataset (African-American and Barbadian, Figures  F2  and F3 ), and in previous studies  (3, 4, 10, 36, 59).
The genetic heritage of continental Europe in Cabo Verde After the initial settlement of Cabo Verde by Portuguese migrants, temporary changes of European dominion in certain islands, newly developing commercial opportunities, and intense piracy during the 16 th and 17 th centuries have triggered different European populations to migrate to the archipelago  (24, 25, 34). Nevertheless, these latter historical events do not seem to have left a major signature in the genetic landscape of Cabo Verde (Figures  F2  and F3 ). Instead, we find that Cabo Verdean-born individuals in our dataset share virtually all their European genetic ancestry with Iberian populations, with extremely limited evidence of contributions from other European regions, consistent with previous studies  (3, 21). Interestingly, the reduced diversity of European populations' contributions to the genomic landscape of Cabo Verde is also identified in other enslaved-African descendant populations in our study, as well as in previous studies in Caribbean and American populations  (11, 12, 36).
1. a. Familial anthropology and geography data The 261 Cabo Verdean individuals were each interviewed to record a classical familial anthropology questionnaire informing self-reported life-history mobility. In particular, we recorded primary residence location, birth location, parental and grand-parental birth and residence locations, and history of islands visited in Cabo Verde and foreign experiences  (21). Furthermore, we also recorded age, sex, marital status, and cumulated years of schooling and higher education (for 211 individuals only), and languages known and their contexts of use. GPS coordinates for each reported location was acquired on site during field-work, supplemented by paper maps and Google Earth TM for foreign locations and islands that we did not visit (Sal and Maio). While participants' birth-locations were often precise, increasing levels of uncertainty arose for the reported parental and grand-parental locations.
1. b. Genome-wide genotyping data The 261 participants each provided 2mL saliva samples collected with DNAGenotek TM OG-250 kits, and complete DNA was extracted following manufacturer's recommendation. DNA samples were genotyped using an Illumina HumanOmni2. 5Million-BeadChip genotyping array following manufacturer's instructions. We followed the quality-control procedures for genotypic data curation using Illumina's GenomeStudio ® Genotyping Module v2. 0 described in  (21). Genotype-calling and population-level quality-controls, and merging procedures are detailed in Figure  SF1. In summary, we extracted a preliminary dataset of 259 Cabo Verdean Kriolu-speaking individuals, including relatives, genotyped at 2,118,835 polymorphic di-allelic autosomal SNPs genome-wide. We then merged this dataset with 2,504 worldwide samples from the 1000 Genomes Project Phase 3 (67); with 1,307 continental African samples from the African Genome Variation Project (68) (EGA accession number EGAD00001000959); and with 1,235 African samples (7) (EGA accession number EGAS00001002078).
We retained only autosomal SNPs common to all data sets, and excluded one individual for each pair of individuals related at the 2 nd degree (at least one grand-parent in common) as inferred with KING (69), following previous procedure  (21). After merging all datasets (Figure  SF1 ), we considered a final working-dataset of 5,157 worldwide unrelated samples, including 233 unrelated Cabo Verdean Kriolu-speaking individuals of which 225 were Cabo-Verde-born, genotyped at 455,705 autosomal bi-allelic SNPs (Figure  F1 ; Table  ST1 ).
2. a. Allele Sharing Dissimilarities, Multidimensional Scaling, and ASD-MDS admixture estimates We calculated pairwise Allele Sharing Dissimilarities (38) using the asd software (v1. 1. 0a; https://github. com/szpiech/asd), among the 5,157 individuals in our worldwide dataset (Figure  F1 ; Table  ST1 ), using 455,705 autosomal SNPs, considering, for a given pair of individuals, only the SNPs with no missing data. We then projected this dissimilarity matrix in three dimensions with metric Multidimensional Scaling using the cmdscale function in R  (72). We conducted successive MDS analyses on different individual subsets of the original ASD matrix, by excluding, in turn, groups of individuals and recomputing each MDS separately. Lists of populations included in each analysis can be found in Table  ST1.
2. b. ADMIXTURE-CLUMPP-DISTRUCT analyses Based on ASD-MDS explorations, we focused on the genetic structure of individuals born in Cabo Verde compared to that of other admixed populations related to TAST migrations. Therefore, we conducted ADMIX-TURE analyses (39) using 1,100 individuals from 22 populations: four from Europe, 14 from Africa, the USA-CEU, the African-American ASW, the Barbadian-ACB populations, and the North Chinese-CHB population as an outgroup (Table  ST1 ). To limit clustering biases due to uneven sampling, we randomly resampled 50 individuals, once, for each one of these 22 populations. Furthermore, we also included all 44 Angolan individuals from four populations in the analyses, as no other samples from the region were available in our dataset. In addition to the 1,100 individuals hence obtained, we included the 225 Cabo Verde-born individuals.
Chromosome painting with ChromoPainter2 We determined the possible origins of each Cabo Verdean, ASW, and ACB individual phased haplotypes among European, African, USA-CEU, and Chinese-CHB populations using CHROMOPAINTER v2  (45)  with the same recombination map used for phasing. Following authors' recommendations, we conducted a first set of 10 replicated analyses on a random subset of 10% of the individuals for chromosomes 2, 5, 12, and 19, which provided a posteriori Ne=233. 933 and θ=0. 0004755376, on average by chromosome weighted by chromosome sizes, to be used in the subsequent analysis. Then, we conducted a full CHROMOPAINTER analysis using these parameters to paint all individuals in our dataset, in turn set as Donor and Recipient. Finally, we combined painted chromosomes for each individual in the Cabo Verdean, ASW, and ACB population, separately.
Estimating possible source populations for the Cabo Verde gene-pool using SOURCEFIND We used CHROMOPAINTER results aggregated for each Cabo Verdean, ACB, and ASW individual separately and conducted two SOURCEFIND  (47)  analyses using all other populations in the dataset as a possible source, separately for four or six possible source populations ("surrogates"). We considered 400,000 MCMC iteration steps (including 100,000 burn-in) and kept only one MCMC step every 10,000 steps for final likelihood estimation. Each individual genome was divided in 100 slots with possibly different ancestry, with an expected number of surrogates equal to 0. 5 times the number of surrogates, for each SOURCEFIND analysis. We aggregated results obtained for all individuals in the ACB, ASW, and each Cabo Verdean birth-island, separately.
3. b. Intersecting ROH and local ancestry painting Using the same phasing results as described above, we conducted 10 EM iterations of the RFMIX2 (78) algorithm to assign, for each Cabo Verdean individual and each SNP on each chromosome, separately, it's putative source population of origin among the 24 African, European, Chinese-CHB, and USA-CEU populations. We collapsed the local ancestry assignments for each SNP in each Cabo Verdean individuals hence obtained into three continental regions, representing broadly African, broadly European, and broadly Asian ancestries respectively. We considered an approach similar to previous work  (50) , and intersected local ancestry calls with ROH calls (Figure  4C-D ). RFMIX2 only makes local ancestry calls at loci that are present in the dataset. Therefore, a gap of unclassified ancestry exists where inferred ancestry switches between two successive genotyped loci as called by RFMIX2.
Inferring genetic admixture histories in Cabo Verde with MetHis-ABC We aimed at reconstructing the detailed genetic admixture history of each Cabo Verde island separately. To do so, we first design MetHis v1. 0 (37) forward-in-time simulations of four competing complex admixture models. We then couple MetHis simulation and summary-statistics calculation tools with ABC Random-Forest scenario-choice implemented in the R package abcrf (54), followed by Neural-Network posterior parameter estimation with the R package abc  (53) , under the winning model for each island separately.
Foundation of the admixed population and duration of the admixture history In all four scenarios (Figure  F6 ; Table  T2 ), we considered, forward-in-time, that the admixed population (Population H) is founded at generation 0, 21 generations before present, which roughly corresponds to the 15 th century for an average generation time of 25 years and sampled individuals born before the 1990s and on average between the 1960's and the 1980's in our dataset. This is reasonable as historical records showed that Cabo Verde was likely un-inhabited before its initial colonial settlement established in the 1460s on Santiago (Albuquerque and Madeira Santos 1991). At generation 0, the admixed population is founded with a proportion sEur,0 of admixed individuals' parents originating from the European source and a proportion sAfr,0 from the African source, with sEur,0 + sAfr,0=1.
For concision here, all the details of these procedures can be found in corresponding Supplementary Text, Tables ST3-13, and Figures SF13-26. Briefly, we performed 10,000 simulations of 60,000 independent SNPs under each of the four competing scenarios described above, drawing parameter values in prior distributions detailed in Table  T2. We then computed 42 summary-statistics on each simulated dataset separately, described in Supplementary Text and Table  ST3 , by drawing sample-sets matching the observed samplesizes of each Cabo Verde birth-island separately. Note that we considered summary-statistics specifically aiming at describing the distribution of individual admixture fractions in each island, known theoretically and empirically to be highly informative about complex admixture history parameters  (37, 56).
Number of simulations and scenario-parameter priors for ABC inference We performed 10,000 such MetHis simulations under each four admixture scenarios for each nine birthislands of Cabo Verde separately. Each simulation was performed under a vector of scenario parameters drawn from prior distributions described in Material and Methods and in Table  T2 , using MetHis parameter generator tools. Separately for each island, we used this set of simulations to determine which scenario best explained the observed data using Random-Forest ABC (see below). Under the winning scenario for each birth-island, we then performed an additional 90,000 simulations to produce a total 100,000 simulations to be used for Neural-Network ABC posterior parameter estimation. Summary statistics for ABC inference ABC inferences use sets of summary statistics calculated on each simulated dataset, as well as the observed data.
Prior-ABC checking Before conducting any ABC inference, we needed to statistically evaluate whether the 42 summarystatistics calculated on the observed datasets were within the range of the sets of summary statistics computed on the simulated datasets with MetHis. To do so, we considered each Cabo Verdean birthisland, in turn as population H, and conducted the following three step procedured whose results are provided in Figures  SF13-22. First, (i) we conducted a goodness-of-fit test between the 40,000 vectors of summary statistics computed for each simulation under the four competing scenarios, and the vector of observed summary statistics with the gfit function of the R package abc  (11) , with 1000 random repetitions and a 1% tolerance level (Figure  SF13A ). Second, (ii) we computed a Principal Component Analysis considering each simulation as an individual and all the summary statistics as observed variables.
Random-Forest ABC scenario-choice We used the Random-Forest ABC algorithm implemented in the abcrf package in R (12,13) for scenario-choice, similarly as in the MetHis-ABC pipeline previously described  (1). We considered the same prior probability for the four competing scenarios. First, for each island separately, we used the abcrf function in this R package to conduct ABC scenario-choice cross-validation of each 40,000 simulations serving in-turn as pseudo-observed data and the remaining 39,999 simulations as training (Figure  SF23A ), using 1000 decision trees in the Random-Forest. We visually checked that prior error rates (the rates of erroneously assigned scenario in the cross-validation) was minimized for this number of decision trees, using the err. abcrf function in the same R package.
Neural Network ABC posterior parameter inference While RF-ABC scenario-choice procedures are specifically designed to accurately work with a relatively small number of simulations compared to classical regression ABC  (12, 14) , posterior jointestimations of all scenario-parameters under the winning scenario remain difficult in Random-Forest  (1, 13). Furthermore, Neural Network ABC posterior parameter inferences have been shown in multiple previous investigations to require substantial amounts of information in the reference table to perform satisfactorily  (1, 11, 15). Therefore, we conducted anew 90,000 simulations with MetHis, for each birthisland separately, under the winning scenario determined with RF-ABC, thus obtaining a total 100,000 simulations under the winning scenario, for each birth-island separately, for further Neural Network ABC posterior parameter estimation using the abc package in R  (11).
Furthermore, for each one of the three tolerance-level values, we tested associated numbers of neurons in the hidden layer between 5 and 11, or 12, the number of free parameters minus one in the winning scenario for each Cabo Verdean island respectively. Then, for each pair of tolerance-level value and number of neurons for each island separately, we performed 1,000 cross-validation NN-ABC posterior parameter inferences using the cv4abc function in the abc package  (11). This procedure draws 1,000 simulations at random and considers them, in turn, as pseudo-observed data for posterior-parameter inference using the remaining 99,999 simulations as the reference table. For the 1,000 pseudo-observed cross-validations and each pair of tolerance level and number of hidden neurons, we calculated the error between the median posterior point-estimate of each parameter ^ and it's known true-value used for simulation , as the mean-square error scaled by the parameter's variance across the 1,000 cross-validations: ∑ ^ -1000 ×  (11).
NN-ABC posterior parameter estimation For each Cabo Verdean island separately under the winning scenario identified with RF-ABC, we jointly estimated the posterior distributions of all parameters using NN-ABC "neuralnet" method option in the function abc of the R package abc  (11) , with logit-transformed parameters ("logit" transformation option) using the tolerance level and number of neurons in the hidden layer identified previously in Table  ST4. For each birth-island separately, a synthetic schematic figure of the complex admixture processes identified using median point estimates of posterior scenario-parameter distribution is.
No other individuals or populations in our analysis show signals of strong resemblance with the East Asian Chinese-CHB for higher values of K. The fourth gray cluster at K=4 is maximized in Southern African individuals from the Soto and Zulu populations. At this value of K, the original orange cluster is maximized in the West Western African Jola from Senegambia and all other African populations exhibit intermediate membership between the gray and the orange cluster. Interestingly, the average membership to the gray "Southern African" cluster decreases with increasing geographic distance from Southern Africa, along the Atlantic Ocean coast of the continent. This echoes the relatively continuous clustering observed in the ASD-MDS analysis for the Atlantic African populations in our dataset (Figures  SF3-5 ).
Interestingly, substantial traces of this cluster are found in YRI, Igbo and ESN East Western African populations as well as ASW and ACB individuals, but virtually absent from Cabo Verdeans or any other African population. This further indicates the closer resemblances of ASW and ACB African genetic component to East Western and West Central Africa than to other African populations in our dataset, as echoed in our ASD-MDS analyses (Figure  F2 , Figures  SF2-6 ). At K=9, the novel light-yellow cluster is maximized in Sierra Leone-MSL population from Central West Africa, albeit imperfectly. This further illustrates the intermediate clustering of Central Western African populations in between Senegambian and East Western African populations, largely overlapping on both sides, and observed in ASD-MDS analyses (Figure  F2 , Figures  SF2-6 ).
As our MetHis-ABC procedures relied exclusively on independent markers, they were a priori not able to capture this haplotypic-based signal, but may nevertheless have been affected. Other scenario misspecifications specific to the African admixture process may further explain this identifiability asymmetry, for instance with more overlapping generations or more assortative mating in the African admixture process than in the European one. Finally, it is plausible that these observed limits of our methods arise from the European-ascertained nature of our genotyping-chip SNP set, despite the LD-pruning here performed, which may alter summary-statistics accurate estimations differently for the African and European sources. All populations and individuals from our working-dataset are included in this analysis: see Table  ST1  for the population list used and Figure  F1  for sample locations and symbols.
ACKNOWLEDGEMENTS The authors would like to warmly thank all Cabo Verdean participants to this study as well as the  UniCV  for facilitating the project. This project was funded in part by the  French "Agence Nationale pour la Recherche " grant  ANR METHIS 15-CE32-0009-1 , and by a grant from the  France-Stanford Center for Interdisciplinary Studies. MB was supported in part by the  Linguistics Department at the University of Michigan (MI, USA). ZAS was supported in part by startup funds from the  Department of Biology at the Pennsylvania State University (PA, USA). CAFL was supported in part by the  Marcus Borgströms Foundation for Genetic Research  and the  Bertil Lundman Foundation for Anthropological Studies (Sweden). We thank the platform " Paléogénomique et génétique moléculaire" (P2GM)  of the  French Muséum National d'Histoire Naturelle at the Musée de l'Homme  for support handling biological samples and generating genetic data.
Notably, Puerto Rican-PUR individuals, also historically descending in part from enslaved-Africans forcibly deported to the Caribbean during the TAST, cluster separately at intermediate distances between the European, Sub-Saharan African, and American clusters along the first three MDS axes (Figure  SF2 ), also consistently with previous observations of substantial three-way admixture between Europe, Africa and the Americas in this population  (16, 17). We explored successive subsamples of this worldwide dataset in order to decompose apparent preferred relationships between each admixed population related to the TAST with respect to African and European populations only. We find (Figure  SF3 ) that the first MDS axis differentiates African and European individuals; the second axis differentiates mainly Senegambian West Western African individuals from Congo Basin hunter-gatherer individuals (namely the Baka, Ba.
Notably, we find Cabo Verdean individuals clustering similarly to Senegambian Fulani individuals in between European and other Senegambian populations, the Fulani being known to be historically admixed between Senegambian and Northern African populations  (18) (19) (20) (21). In the absence of Northern African populations in our dataset, the South-Western European Iberian-IBS population represented the best such proxy-populations with known historical relationship with Northern Africa, hence resulting, at this geographical scale, in the similar clustering of Cabo Verdean and Fulani individuals. Note that Fulani and Cabo Verdean individuals substantially departed from each other along higher MDS axes, the Cabo Verdeans remaining on a European-African axis while the Fulani departed from this trajectory on the European side (Figure  SF4 ).