dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic admixture with local populations influenced population structure in BSP After quality control and merging the generated data of present-day sub-Saharan African populations with publicly available data from a broad range of populations representative of different ethnolinguistic groups (Suppl. Material), we have assembled a dataset for 4,950 individuals from 124 populations (111 sub-Saharan African and 13 Eurasian populations with at least 10 individuals per population), hereafter referred to as the "AfricanNeo" dataset (Fig. S  1. 3-1. 6, Table. S 2). Using four dimensionality reduction methods (Suppl. Material), we have found evidence for fine-scale population structure between sub-Saharan African populations with a clear geographical component (Procrustes correlation to geography > 0.
Population structure in Bantu-speaking populations remains after removing admixture To assess if admixture with local groups is the main process driving spatial patterns of structure in BSP (Fig. 1c, 2a ), we have masked out admixed genomic regions in BSP  33  and kept only west-central African genomic components (Suppl. Material). This masked dataset has allowed us to minimize the influence of non-Bantu-speaker ancestries in subsequent analyses, and the comparison with our results from the original unmasked dataset allowed us to evaluate the impact of admixture on the genetic diversity and population structure of BSP. Principal component analysis (PCA) on the admixture-masked dataset (Fig. 3 , Fig. S 4. 4; Note. S 3) show that BSP retain a clear genetic structure that aligns with geographic (Fig.
3b, d ) (Procrustes correlation > 0. 649, P-value < 0. 001; Fig. 3c-3d ) and linguistic (Fig. 3a, c ) structure, suggesting that processes other than genetic admixture influence spatial patterns of BSP diversity. However, this structure could be driven also by outlier BSP with increased genetic drift. Notably, Herero and Himba from Namibia largely influence PC2 of the admixture-masked dataset (Fig. 3a-3b. S 6) than the other studied BSP (Note. S 4, 5). These signatures can be the consequence of genetic isolation since their arrival in southwestern Africa and recent endogamic practices linked to cattle herding  34  , as suggested in studies of mitochondrial DNA data  35  , genome-wide genotype data  36  , and exome sequencing data  34.
Models underlying the structure of Bantu-speaking populations A strong correlation of genetic relatedness among groups with geography could be explained by an isolation-by-distance (IBD) model, which assumes stepwise gene-flow between neighboring groups. Our dataset of BSP fits an IBD pattern (Fig. 4. However, alternative underlying models could explain IBD patterns. For instance, under a serial-founder model we also expect a strong correlation between shared genetic ancestry and geography. However, in contrast to IBD models, a serial-founder model would also show a decrease in genetic diversity from the putative region of origin. To distinguish between these two models, we have investigated the spatial distribution of three genetic diversity summary statistics (Suppl. Materials) suitable for array-based genotype data  7.
All three statistics (haplotype richness, haplotype heterozygosity, and linkage disequilibrium) support a serial-founder model in which the highest genetic diversity is found in western BSP with a steady decline with distance towards eastern and southern BSP ( Overall, these analyses support the suggestion that the expansion of BSP started from west-central Africa and proceeded mostly via serial bottlenecks to spread throughout sub-equatorial Africa. The fact that a negative correlation between genetic diversity and distance from the source is present even in the unmasked dataset suggests that admixture had a small impact on levels of genetic diversity of BSP, either because there was not that much gene-flow with indigenous groups, or some BSP moved on before they received substantial local gene-flow.
Novel and comprehensive genome-wide dataset as background for future studies Our dataset, analyzed together with published and newly generated aDNA data, shows its potential to provide an effective modern-day background genetic dataset to compare with aDNA individuals (Fig. 1d , Fig. S  12. 1-12. 5). The underlying historical patterns in BSP are very difficult to distinguish based on modern-day data only. Both IBD and serial-founder models can represent more complex underlying population histories among studied BSP, such as multiple overlapping expansions from the same location following similar routes. A clear manifestation of this pattern has been seen in the comparison of European history inferences based on modern DNA  48  and aDNA  49. Analyses such as our Mantel test correlations between linguistics, geography, and genetics tentatively point to complex histories and possible spread-over-spread events (Table.
S 9). In agreement, recent archeological studies also found evidence supporting possible spread-over-spread events for the expansion of BSP  16  (Note. S 1). Future aDNA studies on human remains from different archeological contexts, associated with the Early, Middle, and Late Iron Age, as well as different pottery traditions in Africa, will be necessary for assessing the affinity of the Bantu-speaker-related ancestry to each other and to current-day BSP. Therefore, the availability of our extensive genomic dataset, encompassing the wide geographic range of the expansion of BSP, will enable further testing of these spread-over-spread event hypotheses using aDNA data from Africa.
We find tentative evidence that might point to spread-over-spread events in the demographic history of BSP or genetic admixture between linguistically distantly related BSP. Future aDNA studies in Africa and new spatial modeling methods, using our newly generated dataset as comparative data, will help to refine inferences about the expansions of BSP and subsequent episodes in the population history of ancestral BSP and other African populations with which they interacted. The new findings and generated data will be useful not only to population geneticists, archeologists, historical linguists, anthropologists, and historians focusing on population history in Africa, but also to the medical and health sector studying human genetic variation and human health in African and African-descendant populations.
Acknowledgments We acknowledge and thank all study participants and local fieldwork teams who helped with the collection of samples. The genotyping and sequencing were performed by the  SNP&SEQ Technology Platform, NGI/SciLifeLab Genomics (Sweden). The facility is part of the  National Genomics Infrastructure  supported by the  Swedish Research Council for Infrastructures and Science for Life Laboratory (NGI-SciLifeLab) ,  Sweden. The  SNP&SEQ Technology Platform  is also supported by the  Knut and Alice Wallenberg Foundation. The authors would like to thank  George Mudenda , Director of the  Livingstone Museum , for his support and letter of approval to sample the archeological human remains housed in the Raymond A Dart collection. We are also indebted to the curators of the  Raymond A. Dart Archaeological Human Remains Collection  and the  University of Pretoria Bone Collection  for permission to study the remains.
Data availability: Novel SNP array genotype data of modern-day African populations and whole-genome data of aDNA individuals (bam files) generated in this study will be made available through the European Genome-phenome Archive (EGA) data repository (EGA accessory numbers: TBD). Controlled access policies guided by participant consent agreements will be implemented by the AfricanNeo Data Access Committee. Authorized NIH Data Access Committee (DAC) granted data access to C. M. S. for the controlled-access genetic data analyzed in this study that were previously deposited by Scheinfeldt et al. ,  32  in the NIH dbGAP repository (accession code phs001780. v1. p1; project ID: 21567; date of approval: 2019-05-17), and for data of the Hadza and Sabue populations by Crawford et al. 50  in the NIH dbGAP repository (accession code phs001396.
v1. p1; project ID: 19895; date of approval: 2018-12-18). For the genome-wide genotype data from the Patin et al. ,  9  study (EGA accessory number: EGAD00010001209), data access was granted via European GenomePhenome Archive (EGA) by the GEH Data Access Committee EGAC00001000139. Access to whole-genome sequencing data from the H3Africa Consortium was granted to C. M. S. (EGA accessory numbers: EGAD0000100422, EGAD00001004316, EGAD00001004334, EGAD00001004393, EGAD00001004448, EGAD00001004505, EGAD00001004533, EGAD00001004557, and EGAD00001005076). Code availability: Interactive plots and code used for plotting are available in an online repository (https://github. com/Schlebusch-lab/Expansion_of_BSP_Suppl_Material/blob/main/README. md).
, Fig. S 4. 4). We have thus repeated the PCA on the admixture-masked dataset after excluding Himba and Herero from the analysis, and still observed population structure in the remaining BSP (Fig. S 4. 5). We have then investigated whether BSP harbor signatures of genetic isolation and population size changes potentially driving the observed structure. Most BSP show similar patterns of genetic drift reflected in runs of homozygosity (ROH) (Fig. S 5. 1-5. 12, Table. S 6; Note. S 4; Suppl. Materials), and changes in the effective population sizes (N e ) that support population expansion signatures in the last 10-20 generations (Fig. S 6. 1-6. 2; Note. S 5; Suppl. Materials). The Himba and Herero populations notably deviate from the general patterns of BSP, showing higher intensities of founder events (I f =1.
The economy of Levantine societies in the first millennium BCE was based on animal husbandry and the use of animal life products. New data from Iron Age sites in Lebanon and Syria are analysed in this paper. The investigation of livestock management through the study of archaeological animal bones and their measurements reveals specialised zootechnies. Size variability is observed, especially among sheep and cattle. This heterogeneity suggests that management techniques were directed towards ensuring the necessary supply of animal types to provide the required secondary products in coastal and hinterland, small or large centres. This response to market demands was possible by using different husbandry (zootechnies) strategies and by large-scale trade activities in the region.
Introduction Research on the transition from the Late Bronze Age to the Iron Age in the northern Levant in general has not so far considered faunal and livestock data in the studied proxies. This paper is a first synthesis of the animal economy from the northern Levant during the Iron Age. Such analyses, focusing on zooarchaeological data to reconstruct subsistence patterns and dietary practices, are sorely needed in studies of the Iron Age. Archaeozoological reports from the northern Levant are still relatively uncommon, and while several preliminary studies have been attempted in recent years, the dataset is far from sufficient to pursue general analyses of dietary preferences or animal management strategies throughout the Iron Age (Iron Age I-II-III). The Levantine Iron Age is characterised by a variety of regional patterns and cultural diversity  (Porter, 2016).
Quantification The first point to investigate is the comparison of the faunal spectra from the different sites using the relative frequency of the NISP. The NISP is the available and standard data used for most archaeozoological studies in the region. The total NISP figuring on the histograms in Figs. 2 and 3 represents the sum of all four species: sheep, goats (and unidentified sheep/goats), cattle, and pigs. Therefore, the frequency of each species is calculated from the latter total. The ratio between sheep and goats is calculated using the available NISP. Unfortunately, culling profiles could not be included in this study to identify husbandry practices due to the multiple methods used to record the age of slaughter of animals in each site and to the complete lack of data in a few cases.
Biometrical data In order to identify animal types, we analysed bone measurements from 11 sites with biometric data (the IA sites mentioned in Table  1  and LBA Ugarit). The sites were classified by chronology and the analyses were first conducted between sites from the same period (IA and LBA) for different geographic regions (coastal, hinterland), and secondly by regrouping all the sites from each period according to their geographical region. We analysed the biometric dataset using the Logarithmic Size Index (LSI) method  (Meadow, 1999) , to assess changes in animal size. This method is appropriate for assemblages with very fragmented faunal remains, and scarce measurable skeletal parts. Each measurement is converted into logarithms and then compared to a standard individual (LSI = log X -log standard, with ʻXʼ being the measurement of the archaeological specimen, with ʻstandardʼ being the corresponding measurement of the reference individual).
Three dimensions can be taken on most of the bones according to anatomical orientation; length, width and depth. These dimensions reflect different aspects; length indicates stature, while width and depth are related to body mass  (Pöllath and Peters, 2005). This is the reason why length and width/depth measurements have not been mixed in the analyses. Only fused post-cranial bones and well-developed bones (astragalus) that have reached their maximum size were considered. Due to the difficulties in differentiating sheep and goat phalanges, these bones were not included in the LSI for these taxa, whereas they were considered in the cattle dataset when the identification of the bones was possible (Forelimb/hindlimb according to  (Dottrens, 1946). The nomenclature of measurements is detailed in S.
4 and follows  (Driesch, 1976). Log ratio calculation and analysis were carried out with the package zoolog  (Pozo et al. )  in R 4. 0. 3 (R Core Team, 2020). In zoolog, we chose to extract one grouped log ratio value by length and one by width to avoid overrepresentation of bone elements with the method of priority given to the most frequent measurements. For length, the order of priority is GL, GLl, GLm, and HTC. For width, the order of priority is BT, Bd, Bp, SD, Bfd, and Bfp  (Pozo et al. ). Measurements of bone depth were comparatively rare and not included. The available reference standards were used by default in the Zoolog package for sheep and goats  (Clutton-Brock et al. , 1990) , cattle  (Trentacoste et al. , 2018) , and pigs  (Stopp and Deschler-Erb, 2018). The standard used for sheep (Ovis aries) is the mean measurement of a group of male Soay sheep of known age  (Clutton-Brock et al.
Sheep The analysis of sheep biometry was based on 707 unique bone measurements (158 for length and 549 for width) for IA sites and 1105 for LBA sites (346 for length and 759 for width). The comparison of LSI values by anatomical element shows that archaeological sheep bones do not present any allometry in comparison with the standard used as a reference (S. 2 & S. 3). Most of the archaeological bone remains are fragmented, and thus more width measurements were taken for these remains. The size differences between anatomical elements reflect bone proportions, which can vary between anatomical parts and between breeds  (Davis, 1996). Therefore, we compared the distribution of the LSI by element to ensure that changes in animal sizes were not due to the allometry of an abnormal element. According to the mean and data distribution, sheep LSI values of the calcaneus and astragalus lengths tend to be larger than the radius and metacarpus lengths (Kruskal-Wallis chi-squared = 5.
Discussion The analyses of the dataset presented above show clearly that the As for biometric size, we note an overall increase in the size of sheep with a wide range of diversity on the coast during the Iron Age. Goat size, on the other hand, shows no clear variability between the LBA and IA. As for cattle, the occurrence of two categories (small/large, robust/ slender) is probably related to sexual dimorphism or the presence of hybrid, castrated and/or zebu individuals in the assemblages, mainly for the LBA (see discussion below). For the Iron Age, this division is slightly less clear. Finally, the biometric data for pigs are too scarce to reveal any clear changes.
Indeed, the smaller-sized animals observed, especially sheep, are found in coastal site assemblages. The decrease in size of the three domestic taxa between the Bronze and Iron Age periods is less clear at hinterland sites. The small-sized specimens from the coast that are not found at hinterland sites could result from the arrival of allochthonous animals by sea. This seems obvious for sheep and explains the presence of greater phenotypic variation in size and robustness on the coast than inland. Depending on their origin, certain sheep breeds may have had difficulty adapting to a new environment and different vegetation and stressful On the other hand, there is no difference between the two geographical regions for goats and cattle. The goat data seem to reflect sexual dimorphism rather than the presence of different types at the sites.
The wild goat (C. ibex or C. aegagrus) is attested at Nebi Mend, near Lebanon and Anti-Lebanon Mountains  (Wasse, 2001; Hassan et al. , 2018). The organisation of data for cattle into several groups in the Late Bronze and Iron Ages may result from several factors: sexual dimorphism, the practice of castration, the existence of several cattle breeds, and the presence of the taurus type (Bos taurus), the zebu type (Bos indicus), and their hybrids. The zebu is attested osteologically (bifid vertebrae) and archaeologically (iconography) in Mesopotamia around 1700-1600 BCE  (Matthews, 2002). The earliest identification of the zebu in the Levant (Jordan) dates back to the Late Bronze/Early Iron Age  (Clason, 1978). At Kamid el Loz, sexual dimorphism between males and females does not seem to be significant, according to the author of the study  (Bökönyi, 1990).
In contrast, the zebu was identified at the site based on gracile feet and the presence of bifid vertebrae for the LBA. In addition, it occurs at Nebi Mend in the Late Bronze Age  (Grigson, 2015). At Kamid el Loz, the hypothesis of the presence of three distinct types or breeds at these sites has been suggested: the zebu (Bos cf. indicus), a large bovine, and a small bovine of the humpless type  (Bökönyi, 1990). At Ras Shamra (LBA), the existence of three bovine types had also been discussed  (Vila, 2008; Chahoud and Vila, 2017) , and interpreted as the occurrence of small-sized cows, average-sized oxen and castrated bulls of larger size. It is known from textual sources that one type of cattle was raised for consumption and another type for ploughing work  (Yon, 1997 ). The regional synthesis of metric data shows continuity for the IA and points to the probable presence of more than three groups of cattle.
Certain taxa were not exploited in large centres. Finally, a similar discussion on husbandry patterns is needed for the surrounding regions (e. g. S Anatolia, N Euphrates valley of Mesopotamia, southern Levant) to have a large-scale analysis of the animal economy. The climatic changes often invoked as one of the causes of the collapse at this pivotal transition only have a slight impact on the exploitation of domestic animals. The analyses carried out here identified different phenotypes (size, slender) of domestic animals for Late Bronze and Iron Age settlements. Sheep, goat, and cattle types in coastal and hinterland settlements should be assessed with a large dataset with a better-defined chronology to understand their heterogeneity. More advanced research is needed in extensive regions of the Near East to reconstruct these changes in livestock management and the origin of the different breeds.
Acknowledgements This work was made possible by collaboration with the team of  ARCstarting Grant ZoomWest. We wish to thank  Silvia Valenzuela-Lamas ;  Angela Trentacoste  and  Ariadna Nieto-Espinet  for their help with the use of the package zoolog. Part of the analysis was funded by the  French National Research Agency  ( EvoSheep-ANR-17-CE27-0004 ). The authors thank  Sofian Bouzid  ( Archéorient ,  CNRS-UMR  5133) for his help and discussion on statistical analysis. Finally, we are grateful to  C. Grigson  for accepting to share her dataset of unpublished measurements with us.
6°2Theta. Ca-rich ganomalite is also a component in Zone III (samples 53_2 and 63b_2), but this time accompanied alongside much higher peak intensities of litharge, the tetragonal PbO modification. Massicot occurs subordinately with a weak peak intensity at about 30° 2Theta. Zone II (sample 53. 3) and zone I (sample 53. 4) are nearly coincident and differ only in peak intensity. Here, the predominant phase is litharge. Especially for the samples from zone I and II, but also for the two samples from zone III, it is important to point out the strongly increased 00 l intensities (001, 002) of the litharge compared to the theoretical intensities from the database. This is due to a preferential orientation in the powder samples prepared for XRD analysis of the optically observed lath-like to acicular crystals of zones I and II.
Samples were run alongside standards (oxalic acid and phthalic acid). Graphite targets were dated using the ECHoMICADAS AMS at Gif-sur-Yvette (France). Data reduction was performed by BATS software (version 47)  (Wacker et al. 2010 ). The first few scans were discarded to eliminate possible contamination of the target with ambient atmosphere between target pressing and AMS measurement. Radiocarbon ages were calculated from F 14 C  (Reimer et al. 2004 ), which is corrected for blank and isotopic fractionation for the samples and only isotopic fractionation for blank values. The standard deviation of the blanks is generally less than 10% and represents the counting statistics and C13H correction, which is the smaller error bar on the blank measurements in Figure  2. An overestimated standard deviation of 30% is imposed to the blank value in order to take into account a potential variability of the contaminant which could be added during the sample preparation, which is visible as the larger error bar in Figure  2.
ACKNOWLEDGMENTS We would like to take the opportunity to very warmly thank  Tom Stafford  for sharing his expertise on the XAD method and how to best set this up in our laboratory in the many, many emails sent back and forth. We thank  Xavier Gallet  and  Matthieu Lebon  ( Histoire naturelle de l'Homme préhistorique, Muséum national d'Histoire naturelle, Paris ) for enabling the FTIR-ATR analyses,  Sophie Cersoy  ( Centre de Recherche sur la Conservation, Muséum national d'Histoire naturelle, Paris ) for organising the climate chamber experiment, and  Denis Fiorillo  ( Service de Spectrométrie de Masse Isotopique du Muséum ) for performing the stable isotope analyses. We thank the  Departement of Culture from the Basque Government  and the  Gordailua Center  for enabling us to analyze and sample the Santa Catalina bones, and  Eduardo Berganza  for facilitating the study.
Acknowledging man's infinitesimal power over the world and his own destiny the walking traveller appears utterly at the mercy of events, what Guillaume Thouroude describes as the passive object of the journey: The traveller becomes, so to speak, the one who is being travelled, who no longer masters his route or the duration of his stops  […]. In order to understand this active passivity, one may recall what Gilles Deleuze calls "the power of being affected. "  […]  Power is thus correlated to a form of passivity, that is to say a capacity to welcome affects, to allow oneself to be transformed by them  […] , to invent the rhetorical art that accounts for the acceptance of utter frailty  (Thouroude 2017, 135)   4.