dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Overview of the data. We examined data for over 500,000 autosomal SNPs typed in more than 6,500 individuals born in Brazil, Chile, Colombia, Mexico and Peru (denoted the CAN-DELA sample, Supplementary Fig. 1 ). To infer ancestry in this sample, we also collated data for 2,359 individuals from 117 reference populations (including 430 newly genotyped individuals from 42 populations) representing five major bio-geographic regions: Native Americans; Europeans; East/South Mediterraneans; Sub-Saharan Africans and East Asians (Fig. 1a , Supplementary Table  1 , Supplementary Fig. 2 ). Analysis of these data using the allele-based approach ADMIXTURE  25  shows major limitations for characterising sub-continental ancestry (Supplementary Note 1), similar to what has been observed with other datasets  23, 26.
We therefore performed fully haplotype-based analyses. We first grouped the reference population individuals into 56 homogeneous clusters based on patterns of haplotype sharing, primarily using the program fineSTRUCTURE 23 , followed by secondary refinements (see Methods, Supplementary Tables  2  and 3 ). We inferred the proportion of the genome in each CANDELA individual that is most closely related to each of these 56 surrogate clusters, using a novel approach we term SOURCEFIND (see Methods). In contrast to another haplotypebased approach that implements a Non-Negative least squares (NNLS) regression  24, 27  , SOURCEFIND uses a Bayesian model that eliminates contributions that cannot be reliably distinguished from background noise. Simulations show that SOURCEFIND has greater accuracy than NNLS (Supplementary Note 2).
For ease of visualization, we collapsed the ancestry components inferred from the 56 surrogate clusters into 35 groups, based on the genetic relatedness of the clusters (Supplementary Fig. 3 ). Average continental and sub-continental ancestries from SOURCEFIND and ADMIXTURE are provided in Supplementary Note 3. Patterns of Native American ancestry in the CANDELA dataset. Anthropological studies indicate that Pre-Columbian Native population density varied greatly across the Americas, impacting on the extent of Native American ancestry observed across Latin America 2. Native ancestry in the CANDELA sample varies considerably between countries, and we also observe a marked geographic differentiation in sub-continental Native ancestry within each country, with a strong correspondence with the genetic structure of the Native American reference groups (Figs.
Patterns of Dating admixture from different sources. To assess the timeframe of admixture between the ancestry components described above we used the program GLOBETROTTER  27. Since admixture proportions in Latin Americans vary greatly, we analyzed each individual separately; simulations confirmed the accuracy of GLOBETROTTER in this setting (Supplementary Note 2). Inferred dates for events involving an Iberian source (the initial settlers arriving from Europe and allegedly the first to admix with the Natives) had a median of ten generations (IQR = 7-13), consistent with other estimates for admixture in Latin America  6, 10, 15. Noticeably, individuals with more recent inferred dates of admixture have greater Native ancestry (Fig. 3a , Supplementary Table  4 ), with simulations suggesting this is consistent with continuing admixture between admixed Latin Americans and unadmixed Natives (Supplementary Note 2), possibly as a result of the decline in Iberian immigration after the mid-17th century, concomitant with the demographic recovery of neighboring Native American populations  1, 32.
Compared to inferred dates related to Iberian admixture, admixture events involving non-Iberian European sources (Northwest Europe, Italy) have a significant skew towards more recent dates (Fig. 3b ; Wilcoxon rank-sum test one-sided p-value = 3 × 10 -8 ), consistent with the relatively recent arrival of Germans and Italians  30. East/South Mediterranean ancestry in the CANDELA dataset. SOURCEFIND finds that Sephardic/East/South Mediterranean ancestry is detectable in each country's samples: Brazil (1%), Chile (4%), Colombia (3%), Mexico (3%) and Peru (2%). Altogether, ~23% of the CANDELA individuals show >5% of such ancestry (an average of 12. 2%) (Fig. 1d ) and in these individuals SOURCEFIND infers this ancestry to be mostly Sephardic (7. 3%), with smaller non-Sephardic East Mediterranean (3.
Sub-Saharan African ancestry in the CANDELA dataset. It has been estimated that Brazil received about 4. 2 million African slaves (about half of those brought to the Americas) while Spanish America altogether received about 1. 5 million 3. However, the average Sub-Saharan ancestry in the full CANDELA sample is relatively low (<4%), probably reflecting the fact that regions which historically received large numbers of slaves are underrepresented in this sample (particularly for Brazil, which was sampled mainly in the South of the country)  19. Altogether, ~22% of the individuals studied show more than 5% sub-Saharan African ancestry. SOURCEFIND infers a marked predominance of the West African sub-component, particularly in the Spanish American countries (Supplementary Figures  5  and 6 ), consistent with previous genetic analyses, and with historical information  1, 39.
The distribution of dates involving Sub-Saharan African admixture mostly overlaps with that for Iberian admixture, although a substantial proportion of recent dates were also inferred (Fig. 3d ), possibly reflecting continuing African admixture in the regions sampled. East Asian ancestry in the CANDELA dataset. Other than the major Native American, European/Mediterranean and sub-Saharan African ancestry components, historical information indicates some East Asian migration to Latin America, particularly after independence in the 19th century  30. SOURCEFIND estimates East Asian ancestry in the CANDELA sample to be, on average, very low (<1%) in Brazil, Chile, Colombia, Mexico, and slightly higher in Peru (1. 4%). In individuals with >5% East Asian ancestry, this component is inferred to be most closely related to the Chinese and to a lesser extent the Japanese, except in Brazil where the opposite is found (Supplementary Fig.
Methods Genotype datasets. The CANDELA dataset (http://www. ucl. ac. uk/candela) consists of genotypes from 6,852 individuals ascertained in five Latin American countries (Brazil N = 676, Chile N = 1,891, Colombia N = 1,713, Mexico N = 1,288 and Peru N = 1,284) (Supplementary Fig. 1 ). This study sample has been described in detail in Ruiz-Linares et al. 19. Briefly, adult individuals of both sexes were ascertained at one main recruitment site per country (Porto Alegre in Brazil, Arica in Chile, Medellín in Colombia, Mexico City in Mexico, and Lima in Peru). Informed consent was obtained from all participants, complying with all relevant ethical regulations as approved by Universidad Nacional Autónoma de México (México), Universidad de Antioquia (Colombia), Universidad Perúana Cayetano Heredia (Perú), Universidad de Tarapacá (Chile), Universidade Federal do Rio Grande do Sul (Brazil) and University College London (UK).
A structured interview recorded the birthplace of volunteers and their ancestors (up to grandparents), as well as information on the language(s) spoken by them. We have previously reported genome-wide association studies based on Illumina Huma-nOmniExpress chip data obtained in these individuals  [20] [21] [22]. To perform ancestry analyses in the CANDELA individuals we collated a reference population dataset from regions having potentially contributed to admixture in Latin America. We combined publicly available data  [46] [47] [48] [49] [50]  with data from newly genotyped samples obtained here (Fig. 1 , Supplementary Table  1 , Supplementary Fig. 2 ). Altogether we collated data for 2,359 individuals from 117 reference populations (38 Native American, 42 European, 15 East/South Mediterranean, 15 Sub-Saharan African, and 7 East Asian).
Of these, 42 were newly genotyped population samples (comprising 27 Native American, seven European, and eight East/South Mediterranean), including a total of 430 individuals. These individuals were genotyped on the Illumina HumanOmniExpress chip which includes 730,525 SNPs. PLINK v1. 9  51, 52  was used to exclude SNPs and individuals with more than 5% missing data, markers with minor-allele frequency <1%, related individuals, and those who failed the X-chromosome sex concordance check. The same QC filters had been applied to the CANDELA dataset  [20] [21] [22]. Individuals born outside the country were relocated when coming from one of the five countries included in this study or otherwise removed. Similar quality controls were applied to the public reference population datasets. In addition, unsupervised ADMIXTURE  25  analyses of reference population samples were used to identify and exclude Sub-Saharan Africans, East Asians, and Europeans with less than 95% of their own continental ancestry.
After QC, the merged CANDELA plus reference population dataset comprised genotypes for 546,780 autosomal SNPs in 8,647 individuals (including 6,589 Latin Americans and 2,058 individuals from the reference population samples). A global overview of our analysis strategy is provided in Supplementary Figure  12. Phasing of genotype data. Phasing of the merged dataset was performed with SHAPEIT2 53 using default parameters. Genetic distances used were obtained from the HapMap Phase II genetic map build GRCh37  54. Missing genotypes for any SNP (<5% after the QC) were imputed during the phasing process. Inference of haplotype similarity patterns. CHROMOPAINTER  23  was used to infer haplotype similarity (informally, chromosome painting) and estimate the proportion of DNA shared between donor and recipient individuals, thus generating a coancestry matrix relating individuals.
CHROMOPAINTER was setup so that the donors were exclusively reference population individuals, while recipients included CANDELA individuals as well as reference population individuals. Consideration of reference population individuals as recipients as well as donors enabled the analysis of genetic structure in the reference population samples (using fineSTRUCTURE as described below), while the analysis of coancestry between CANDELA recipients and reference population donors enabled the inference of ancestry profiles in the CANDELA individuals from donor clusters (using SOURCEFIND as described below). The recombination scaling constant N e and the mutation parameter θ used by CHROMOPAINTER were jointly estimated for every individual in a subset of chromosomes (1, 6, 13, and 22) with 10 Expectation-Maximization steps, starting from default values defined by the software.
The average N e and θ values across chromosomes (weighted by chromosome size) were then used for subsequent CHROMOPAINTER runs on all autosomes (N e = 290. 83 and θ = 0. 00038). Genetic distances from the HapMap Phase II genetic map build GRCh37 were used in the CHROMOPAINTER runs. CANDELA individuals with >99% European ancestry (52 Brazilians, of which 37 reported German and 15 Italian ancestors) or with >95% Native American ancestry (1 Colombian, 22 Mexicans, 65 Chileans and 17 Peruvians) were included amongst the donors as they may harbor ancestry components not present in our reference dataset. In Supplementary Note 6 we show that our conclusions about ancestry are similar if these CANDELA individuals are excluded from the reference dataset. In total, 157 CANDELA individuals and 1,942 reference individuals were added to the panel of donors, for a total of 2,099 samples.
The remaining 116 individuals from the initial reference dataset were excluded. Of these 80 were Native Americans with less than 95% Native ancestry, and 36 were Native Americans excluded after the haplotype-based clustering analyses performed to select the reference panel for the ancestry inference, as explained in the next section. Definition of clusters of reference population individuals. To evaluate genetic structure in the reference populations independent of sample labels we used fineSTRUCTURE  23  , a program that defines homogeneous clusters of individuals based on the coancestry matrix produced by CHROMOPAINTER. We performed additional analyses on the clusters defined by fineSTRUCTURE in order to select a final subset of donor clusters to be used as surrogates for the unknown populations that historically contributed ancestry to Latin Americans (we refer to this subset as surrogate clusters or surrogates).
59  , but introduces new improvements in the way that λ is estimated and in the MCMC proposal procedure. Estimation of the number of generations since admixture. The times and sources of major admixture events were inferred using the program GLOBE-TROTTER  27. GLOBETROTTER tests for evidence of one or more pulses of admixture between two or more ancestral groups, and dates these admixture events and infers the genetic make-up of the admixing groups involved. Due to the recent nature of intermixing in the Americas, admixture times and proportions may vary substantially across CANDELA individuals. Therefore we tested each individual separately, restricting this analysis to the 6,352 individuals inferred by SOURCE-FIND to have ancestry from more than one surrogate cluster. For each haploid genome of each individual, we used ten random samples of genome-wide local matching to donor clusters per haploid as provided by the CHROMOPAINTER analysis described above.
As we have relatively few reference individuals with Mapuche and Central Andean ancestry, we inferred allele frequencies by combining these reference samples with admixed Candela individuals that were inferred to carry the appropriate Native ancestry at these loci. To do so, we used the software RFMix 61 to infer local continental ancestry in the subset of phased CANDELA individuals described earlier. Three continental reference panels (consisting of phased haplotypes for 107 IBS (Iberian Population in Spain; 1000 Genomes Project), 101 YRI (Yoruba in Ibadan, Nigeria; 1000 Genomes Project) and 125 Native American samples) were used for this purpose. RFMix assigns local continental ancestry to each allele of each CANDELA haplotype, allowing for errors in genotyping, slight admixture in the reference samples, etc.
Acknowledgements We would like to dedicate this paper to the memory of L. L. Cavalli-Sforza , a wonderful mentor and a constant source of inspiration. We are grateful to the volunteers for supporting this research. We thank  Alvaro Alvarado ,  William Arias ,  Mónica Ballesteros Romero ,  Ricardo Cebrecos ,  Miguel Ángel Contreras Sieck ,  Francisco de Ávila Becerril ,  Joyce De la Piedra ,  María Teresa Del Solar ,  Gastón Macín ,  William Flores ,  Martha Granados Riveros ,  Rosilene Paim ,  Ricardo Gunski ,  Sergeant João Felisberto Menezes Cavalheiro ,  Major Eugênio Correa de Souza Junior ,  Wendy Hart ,  Ilich Jafet Moreno ,  Claudia Jaramillo ,  Paola León-Mimila ,  Francisco Quispealaya ,  Diana Rogel Diaz ,  Ruth Rojas  and  Vanessa Sarabia , for assistance with volunteer recruitment, sample processing and data entry.
We acknowledge the institutions that provided facilities for the assessment of volunteers:  Escuela Nacional de Antropología e Historia  and  Universidad Nacional Autónoma de México (México) ;  Universidade Federal do Rio Grande do Sul (Brazil) ;  13°Companhia de Comunicações Mecanizada do Exército Brasileiro (Brazil) ;  Pontificia Universidad Católica del Perú ,  Universidad de Lima  and  Universidad Nacional Mayor de San Marcos (Perú). We also thank the  National Laboratory for the Genetics of Israeli Populations  (http://yoran. tau. ac. il/nlgip/) and  Dr. David Gurwitz  for making available DNA samples. We thank  Chris Tyler-Smith  and  Caroline Costedoat  for comments on the manuscript. Work leading to this publication was funded by grants from: the  Leverhulme Trust  ( F/07 134/DF ),  BBSRC  ( BB/I021213/1 ), the  Excellence Initiative of Aix-Marseille University-A*MIDEX  (a  French Investissements d'Avenir programme) ,  Wellcome Trust/Royal Society  ( 098386/Z/12/Z  to G.
Data availability Raw genotype or phenotype data cannot be made available due to restrictions imposed by the ethics approval. Summary statistics from previous GWAS on the CANDELA consortium data have been deposited in GWAS central [https://www. gwascentral. org/study/HGVST1841/, http://www. gwascentral. org/study/ HGVST3308]. The publicly available reference population datasets were be obtained from: (1) Mallick et al. 48 : EBI ENA PRJEB9586; (2) Eichstaedt et al. 47 : NCBI GEO GSE55175; (3) 1000 Genomes Project 46 [http://www. internationalgenome. org/data]; (4) Pagani et al. 49 : [http://mega. bioanth. cam. ac. uk/ data/Ethiopia/] and (5) Schlebusch et al. 50 : [http://jakobssonlab. iob. uu. se/data/].
European ancestry in the CANDELA dataset. Native American a European b Pima Nahua2 Nahua1 Mayan South- Mixe Mexico Chibcha Paez Amazon AndesPiedmont Quechua1 Aymara Chaco1 5% Canarylslands 5% Quechua2 20% Portugal/WestSpain 20% Colla 75% 50% CentralSouthSpain CentralNorthSpain Basque 75% 50% 100% Catalonia 100% Mapuche Italy North-WestEurope North-EastEurope East/South Mediterranean c 5% 20% 50% 75% 100% Sephardic EastMediterranean SouthMediterranean Spanish American countries (Fig. 1c). In Brazil the predominant European sub-component matches mostly the Portugal/West- Spain reference group while in Mexico, Colombia, Peru and Chile it is mostly Central/South-Spanish ancestry that is inferred Importantly, SOURCEFIND distinguishes between closely- related ancestry components from the Iberian Peninsula, as well as from the East and South Mediterranean (including individuals self-identified as Sephardic; i.
Density Functional Theory (DFT) has become the quasi-standard for ab-initio simulations for a wide range of applications. While the intrinsic cubic scaling of DFT was for a long time limiting the accessible system size to some hundred atoms, the recent progress with respect to linear scaling DFT methods has allowed to tackle problems that are larger by many orders of magnitudes. However, as these linear scaling methods were developed for insulators, they cannot, in general, be straightforwardly applied to metals, as a finite temperature is needed to ensure locality of the density matrix. In this paper we show that, once finite electronic temperature is employed, the linear scaling version of the BigDFT code is able to exploit this locality to provide a computational treatment that scales linearly with respect to the number of atoms of a metallic system.
When such kind of information is required, like for example when studying the arrangement of electrons close to a defective region, investigation techniques like the ones presented above are or utmost importance. In this paper we report on the capabilities of BigDFT to perform reduced and eventually also linear scaling calculations for a metallic system at finite temperature. Whereas previous publications have highlighted in detail the accuracy, efficiency and linear scaling capabilities of this code for systems with a finite HOMO-LUMO gap  43, 44  , metals have so far not been considered. In this paper we demonstrate that the basic algorithm of BigDFT remains stable also for systems with vanishing gap and thus allows to routinely perform accurate linear scaling calculation for large metallic systems without the need of additional adjustments.
I, and thus justifies the exploitation of the nearsightedness principle. Nevertheless, linear scaling calculations for metals remain very challenging. First of all, the used electronic temperatures must not be too large -otherwise one would change the physics in a too drastic way -and thus the density matrix decays much slower compared to finite gap systems. Moreover, the vanishing gap complicates the calculation of the density kernel. In BigDFT, we use for this task the CheSS library 55 -one of the building-blocks of the BigDFT program suite -that offers several different solvers. In the Fermi Operator Expansion (FOE) method  14, 15  , which is the linear scaling solver available within CheSS, one has to approximate the function that assigns the occupation numbers -typically the Fermi function -with Chebyshev polynomials.
Energy versus volume As a first test we demonstrate that the linear scaling version can accurately calculate the equation of state relating energy and volume. To this end, we scaled the lattice vectors of the Tungsten system by ±4% around its equilibrium value. We compare four different setups: (1)/(2) the linear scaling version of BigDFT using a 9x9x9 supercell (containing 1458 atoms) and no k-points, using as solver both diagonalization (DIAG) and FOE; (3) the cubic scaling version of BigDFT using the 2atoms unit cell and a 9x9x9 k-point mesh; (4) the same setup as (  3 ), but run with the Abinit code  [61] [62] [63] [64]. In Fig. 3  we compare the energy-vs-volume curves for all four setups. In the same figure we also show, for all four setups, the variation of the pressure as a function of the volume.
IV. CONCLUSIONS AND OUTLOOK In this work we have demonstrated, by applying our code BigDFT to the case of large Tungsten systems, that it is possible to perform accurate and efficient linear scaling DFT calculations for metals with this code. Even though the linear scaling version of BigDFT was designed -as most other codes -for insulating systems, the obtained performance -considering also the very high accuracy of the resulting description -for metallic systems is excellent. We have shown that the results obtained with the linear scaling version of BigDFT are of equal quality as those obtained with traditional cubic scaling approaches, but the reduced scaling allows to tackle much larger systems. The crossover point between the cubic and linear scaling treatment lies at about 500 atoms. Thanks to these achievements, the possibility of addressing the challenge of unbiased first-principles investigation for systems with such a large degree of complexity opens up new interesting opportunities, as now more realistic conditions, like for instance lower concentrations of supercell defects, can be considered.
V. ACKNOWLEDGMENTS We acknowledge valuable discussions with  María José Caturla  and  Chu-Chun Fu. S. M. acknowledges support from the  MaX  project, which has received funding from the  European Unions Horizon 2020 Research and Innovation Programme  under Grant Agreements 676598. M. A. acknowledges support from the  Novartis Universität Basel Excellence Scholarship for Life Sciences  and the  Swiss National Science Foundation  ( P300P2-158407 ,  P300P2-174475 ). We gratefully acknowledge the computing resources on  Marconi-Fusion  under the  EUROfusion  project  BigDFT4F , from the  Swiss National Supercomputing Center in Lugano  (project  s700 ), the  Extreme Science and Engineering Discovery Environment (XSEDE)  (which is supported by  National Science Foundation  grant number  OCI-1053575 ), the Bridges system at the  Pittsburgh Supercomputing Center (PSC)  (which is supported by  NSF  award number  ACI-1445606 ), the Quest high performance computing facility at  Northwestern University , and the  National Energy Research Scientific Computing Center  (DOE:  DE-AC02-05CH11231 ).
Phenocam data would also allow ICOS to explore in more detail relationships between phenological events, climatic variables and soil moisture states in order to develop hypotheses about the impacts of climate change on phenology, ecosystem carbon uptake and water losses. Moreover, the ICOS framework can help ensure a standardised dataset that can serve as both a calibration and validation dataset for remote sensing data or products and various modelling efforts. In addition, the intuitive nature of the phenocam imagery and subsequent processing allows for easy outreach and integration in education programs across Europe. Here, we provide a practical guideline on how to install phenocams at stations within and beyond ICOS to ensure optimal quality in the acquired images within the context of the ICOS network.