dataesr/scientific-paragraphs-categorization huggingface dataset:
For both of these sample sets, we include a "verbose. ind" list of populations that includes verbose sample identifiers which correspond to the 255 populations listed in Table  S9. 4 and plotted in Figure  1A. We also release a "simple. ind" list of sample identifier which corresponds to the merged groups of 216 populations and simpler names used for most of the analyses in the study. We note that in practice, for each of these two sets of samples we are releasing 14 genotyping datasets: 14 genotype files and SNP lists. The file that the majority of researchers are likely to wish to use is the "allsnps" dataset that includes all SNPs. However, we also release separately SNP datasets for each of the 14 panels for researchers who wish to take advantage of the uniform ascertainment.
Merging in of ancient samples and whole genome sequences We next merged in 22 samples into the genotyping dataset whose data were obtained by sequencing (either the human reference genome sequence "Href", several primates, archaic genome sequences (Neandertals and Denisovans), or ancient modern humans (Table  S9. 5). Many of the analyses reported in this study include ancient samples that had too-low sequencing coverage to permit confident diploid genotype calls. To analyze these samples in conjunction with genotyping data from the Affymetrix Human Origins array, we picked a single allele at random for each individual from each site in the genome for which there was a high quality sequence. That allele was then used to represent that individual at that nucleotide position (the individual was treated as homozygous there).
ADMIXTURE analysis We carried out model-based clustering analysis using ADMIXTURE  7  1. 23 on the dataset, combining the present-day humans with Loschbour, Stuttgart, Motala12, Motala_merge, and LaBrana. ADMIXTURE is a commonly used method for investigating admixture proportions in human populations, although its interpretation in terms of history is not straightforward. ADMIXTURE searches for a specified number (K) "ancestral populations" described by allele frequencies, and then assigns each study sample coefficients of ancestry from these reconstructed populations. While ADMIXTURE is a commonly used method for revealing genetic structure in a set of samples, it is blind to the genesis of the structure. Two populations showing shared membership to a particular cluster might do so due to either shared ancestry or recent admixture.
Nevertheless, ADMIXTURE is useful as an exploratory tool in analyses of genetic structure, and in the context of the present paper we use it mainly to (i) identify a set of West Eurasian populations for further analysis, and (ii) to identify a set of non-West Eurasian populations from the rest of the world to be used as references for our methods of ancestry estimation. This analysis also serves as an exploration of populations included in the Affymetrix Human Origins Array dataset made available with this paper. We used PLINK  16  1. 07 to thin the original dataset of 594,924 autosomal SNPS to remove SNPs in strong linkage disequilibrium, employing a window of 200 SNPs advanced by 25 SNPs and an r 2 threshold of 0. 4 (--indep-pairwise 200 25 0. 4). A total of 291,184 SNPs remained for analysis after this procedure.
(2008) 8 (Table  S10. 1) and contrasted the fraction of individuals from each region in our Human Origins dataset to that used in that paper. We include in Table  S10. 1 Cypriot and Turkish populations which are not included in Fig. S10. 5. While these samples do not appear to cluster with Europeans in any of our analyses, Cypriot and Turkish samples were used in Novembre et al. (2008), and so we include them for consistency in the comparison. Our results highlight the fact that the correspondence between the map of Europe and the first two principal components depends on the composition of the included samples. Unfortunately, the lack of coverage of C and opposite balance of W/NW and E/NE in the two studies does not allow us to "mimic" the distribution of Novembre et al. (2008) and to test if the genetic-geographic correspondence might emerge in our dataset.
For each West Eurasian population, we computed all 19,306 possible f 3 -statistics for all pairs of references that included: 192 other present-day human populations from the Affymetrix Human Origins dataset (SI9) as well as the five ancient individuals: Loschbour and LaBrana (WHG), MA1 (ANE), Stuttgart (ANE), and Motala12 (SHG). Question (i) is definitively answered in our analysis by our finding that when we search for pairs of populations that produces the lowest f 3 -statistic for each target population in Europe, the lowest statistic is usually highly significantly negative even after correction for multiple hypothesis testing (Table  1 ). We note that we have previously reported widespread evidence of mixture via f 3 -statistics in Europeans  1. What is new here is the larger number of European populations analyzed, and the fact that with the ancient genomes in hand, we can show that some of the ancestral mixing populations are phylogenetically more closely related to the ancient genomes than to any present-day human populations in our dataset.
We anticipate that with larger population sample sizes additional minor inputs into Europe may be identified, further refining the history of European populations beyond the three ancestral populations identified by our study. However, these results increase our confidence that a model of three ancestral inputs can explain important features of the data. A history of such admixture is plausible archaeologically, as the Linearbandkeramik postdates the earliest Neolithic of southeastern Europe, and there may have been opportunity for Near Eastern and Oceanians because of their Denisovan ancestry, neither of which conforms to the Fig. S13  We choose Yoruba as a source of the African admixture as the source of the admixture in BedouinB appears to be African-farmer related (K=5, SI 9), and Yoruba are the population of African farmers with the highest sample size in the Human Origins dataset.
S14. 12 form a cline from Stuttgart in the south, to Lithuanians and Estonians in the north, but the three populations violating our model (Table  S14. 13) are clearly to the right, sharing relatively more alleles with the Han. We also add the single Saami individual from our dataset and the Chuvash on this plot. These two additional European groups deviate from the main European cline even more strongly, in the same direction as the Finnish, Mordovian, and Russian. While we find no evidence that the Han have West Eurasian admixture (SI9), it is still possible in principle that they share some unknown common ancestry with Northeast Europeans. We also computed the statistic" f 3 (Mbuti; Ami, Test) statistic for West Eurasian populations. This "outgroup f 3statistic" 6 measures the amount of common genetic drift shared between Ami (a Taiwanese aboriginal population that seems extremely unlikely to have historical connections with Northeastern Europeans in particular).
3,  S18. 4). This is consistent with these two populations being deeply related so that they have correlated levels of shared IBD to non-West Eurasian populations (e. g. Africans or eastern non-Africans). Loschbour shares a slightly higher number of IBD tracts with the present-day populations that happen to be in the POPRES dataset than does Stuttgart (3. 18 vs. 3. 01, respectively). In addition, slightly more of Loschbour's genome can be found in IBD tracts (4. 45x10 6 bp vs. 4. 13x10 6 bp, respectively). This analysis is qualitatively consistent with the IBD analysis of SI 18 which is performed on a different dataset. Both analyses would benefit from a high quality genome of an individual harboring substantial Ancient North Eurasian ancestry from a more recent period; this would allow the study of haplotype sharing between such an individual and present-day West Eurasian populations.
Acknowledgments We are grateful to  Cynthia Beall ,  Neil Bradman ,  Amha Gebremedhin ,  Damian Labuda ,  Maria Nelis  and  Anna Di Rienzo  for sharing DNA samples; to  Detlef Weigel ,  Christa Lanz ,  Verena Schünemann ,  Peter Bauer  and  Olaf Riess  for support and access to DNA sequencing facilities; to  Philip Johnson  for advice on contamination estimation; and to  Pontus Skoglund  for sharing the graphics software that we used to generate Extended Data Fig. 6. We thank  Kenneth Nordtvedt  for alerting us about the existence of newly discovered Y-chromosome SNPs. The collections and methods for the Population Reference Sample (POPRES) discussed in SI18 are described in ref. 42 , and the dataset used for our analyses was obtained from dbGaP at http://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study.
cgi?study_id=phs000145. v4. p2 through dbGaP accession number phs000145. v1. p2. We thank all the volunteers who donated DNA; the staff of the  Unità Operativa Complessa di Medicina Trasfusionale, Azienda Ospedaliera Umberto I, Siracusa, Italy  for assistance in sample collection; and  The National Laboratory  for This work provides a first glimpse into the pre-Neolithic Y chromosomes of  Europe. Unlike the La Braña  male  19  , a Mesolithic Iberian whose C-V20 chromosome is extremely rare in present-day Europe  20  , the  Y-chromosomes of Loschbour  and the Motala males appear to belong to haplogroups that persist in a substantial fraction of present Europeans. Despite the fact that our sample is limited to two locations and five male individuals, the results in this section are consistent with haplogroup I representing a major pre-Neolithic European clade, and hint at subsequent events during and after the Neolithic transition as important contributors to the Y chromosomal variation of living Europeans.
1-ŠMM-07-K-01-013 ),funded by the  European Social Fund  under the  Global Grant Measure. AS was supported by  Spanish  grants  SAF2008-02971  and  EM 2012/045. OU was supported by  Ukrainian SFFS  grant  F53. 4/071. SAT was supported by  NIH Pioneer  Award  8DP1ES022577-04  and  NSF   HOMINID award   BCS-0827436. KT was supported by an  Indian CSIR Network Project  (GENESIS:  BSC0121 ). LS was supported by an  Indian CSIR Bhatnagar Fellowship. RV, MM, JP and EM were supported by the  European Union Regional Development Fund through the Centre of Excellence in Genomics  to the  Estonian Biocentre and University of Tartu  and by a  Estonian Basic Research  grant  SF0270177As08. MM was additionally supported by  Estonian Science Foundation  grant # 8973. JGS and MS were supported by  NIH  grant  GM40282.
Supplementary Information 1 Sampling, Library Preparation and Sequencing Alissa Mittnik*, Susanna Sawyer, Ruth Bollongino, Christos Economou, Dominique Delsate, Michael Francken, Joachim Wahl, Johannes Krause * To whom correspondence should be addressed (amittnik@gmail. com) Table of Contents 1 SI 1 -Sampling, library preparation and sequencing 2-8 SI 2 -Processing of sequencing data and estimation of heterozygosity 9-16 SI 3 -Ancient DNA authenticity 17-23 SI 4 -Mitochondrial genome analysis 24-29 SI 5 -Sex determination and Y chromosome analysis 30-36 SI 6 -Neanderthal ancestry estimates in the ancient genomes 37-38 SI 7 -Analysis of segmental duplications and copy number variants 39-41 SI 8 -Phenotypic inference 42-53 SI 9 -Affymetrix Human Origins genotyping dataset and ADMIXTURE analysis 54-70 SI 10 -Principal Components Analysis 71-78 SI 11 -All-pair f 3 -statistics 79-82 SI 12 -Statistical evidence for at least three source populations for present-day Europeans 83-86 SI 13 -Admixture proportions for Stuttgart 87-90 SI 14 -Admixture graph modeling 91-127 SI 15 -MixMapper analysis of population relationships 128-132 SI 16 -TreeMix analysis of population relationships 133-141 SI 17 -Admixture estimates that do not require phylogenetic modeling 142-145 SI 18 -Segments identical due to shared descent between modern and ancient samples 146-149 SI 19 -ChromoPainter / fineSTRUCTURE analysis 150-152.
Additionally, the Web applications derived from MDWE approaches pose a rigid/static architecture that cannot be easily changed hindering scalability fixes. To make things worse, diagnosing and fixing these problems becomes cumbersome and impossible to be done in the models thus forcing teams to deal with the generated code. To overcome these problems, a recent study  [7]  has captured the top 20 problems that Web applications face to achieve scalability and it also shows that "unforeseen scalability issues during development, can easily appear in a production environment". This list includes obstacles that affect both code and model based development and includes aspects such scalability is a desired aspe the application to run fast ( technical aspects that need t Identifying a scalability can be easy, however diagn sumes many human resour software development effor not help on that aspect a until their primitives and a code, thus breaking the mod improvement claimed by M as monitoring, logging and caching.
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Background: Examining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose. Methods: Geographical grids based on the Lambert Azimuthal Equal Area projection are well suited for spatial epidemiology because they preserve area: each cell of the grid has the same area.
After the hypothesis is chosen, the data is examined to see whether or not it supports the hypothesis. In practice, both approaches benefit from the popularization of geographic information systems. Addresses of patients with a disease of interest are routinely geolocalized, dozens of databases inform on air and water quality, soil cover, climate, local social environment, etc. with an always increasing spatial resolution. Using exact locations (as opposed to grouping by administrative units, as in the past) comes at a computational cost, which can be quite large particularly for cluster searches. A popular method for performing a cluster search involves moving a shape (called a scanning window) across the region of study and counting the number of cases and controls in the scanning window.
Projecting the data onto the geographical grid Epidemiological data generally involves geographical coordinates encoded in the WGS84 coordinate system. WGS84 is the World Geodetic System established in 1984, which is well known through its use in the Global Positioning System (GPS). To project WGS84 coordinates to a grid based on the Lambert Azimuthal Equal Area (LAEA) projection system, the first step is to convert WGS84 coordinates to LAEA coordinates. This coordinate transformation can be done with the GDAL (Geospatial Data Abstraction Library) see  [17]. One needs to know the EPSG codes of the source and target coordinate systems, which are (EPSG: 4326) for WGS84 and (EPSG: 3035) for ETRS89-LAEA. We provide a python script with which users can project their WGS84 coordinate data to ETRS89-LAEA grid coordinates for use in FGBASE software.
The software FGBASE displays the geographical grid as an interactive map (see Figure  2 ). Representing the points of interest in the hypothesis driven approach The grid-based approach is well suited Figure  1  Comparison of the use of point locations with distance computations versus the use of the grid-based approach when searching for clusters. (Above): Setting where cases and controls data consists of points (identified by latitude and longitude). Determining the number of cases and controls in a (circular) region requires computing the distances from the center of the region to each case and each control. For cluster searches, millions of regions (the potential clusters) are considered which means billions of distance calculations for a medium size (case/control) study. (Below): Setting where cases and controls have been projected to a grid.
The environmental factors Figure  2  Interactive map of the software FGBASE. The interactive map (centered here on the city of Le Havre, France) displays the cells of the grid. These square cells are color coded according to user defined criteria. Here the following color codes are used: light grey if the square is uninhabited, dark grey if the square is inhabited, purple if the square contains cases of the disease, yellow if the square contains 2 or more cases and the population to case ratio of the square is below 1500. The map has a yellow selection rectangle, which the user can move and resize. The panel below the map displays statistics calculated for the region enclosed in the selection rectangle. The user can load a list of cities with their geographical cordinates, for easy access through the left panel.
Using FGBASE to find cluster of cases using the data-driven option In this option, the locations of cases are examined in the context of either a set of control locations or an underlying population density. A cluster search is performed to determine areas where the number of cases is much larger than would be expected by chance. Two datasets are necessary: (i) locations of cases (ii) locations of controls or appropriate population density data. For example, for all European countries, population grids can be downloaded from the European Commission's Eurostat website  [16]. (For non-European countries a solution could be to use the LandScan™ population density distribution see  [18] , but the user must ensure his/her cases and controls are projected to the same grid as the one used by LandScan™).
A case study with FGBASE: patients with type 1 diabetes (T1D) The development of FGBASE was driven by a program of search of possible environmental factors of Type 1 Diabetes (T1D). Indeed, in several European countries, the incidence of T1D continues to progress rapidly and has doubled since the 80′s in children aged less than 5 years  [19]. The reason for this cannot be genetic as the observed variations occurred during a short time when the genetic structure of the population did not change. Numerous case-control studies of environmental associations with T1D have examined specific candidate factor approaches (  [20] [21] [22] [23] ) but no single factor has gained further credit in the causation of T1D (  [24, 25] ). The population used in this example is made with the participants of "Isis-Diab", an ongoing prospective cohort of T1D patients recruited since 2007 by the Isis-Diab Network composed of 99 diabetic centers covering almost all French regions (see description and list of participating centers in Additional file 1: Table  S1 ).
The main objective of the Isis-Diab program is the exploration of environmental and gene-environment interaction in patients with T1D. Inclusion criteria for the current study were T1D occurring in children aged less than 15 years. T1D was defined according to the American Diabetes Association  [26] , and by positive autoantibodies to GAD, insulin, and/ or IA2. All studied patients were born in France. Data consist of clinical, genomic, and environmental exposures. Here, in the example shown to exemplify the "hypothesis" driven option of FGBASE, we do not consider the clinical and genetic characteristics of the patients and we focus on a single source of pollution described in the next section.
The environmental exposures analyzed In France, all industrial polluting industries are registered and must provide data on the polluting emissions they are responsible of. Data are in the IREP database (see  [27] ) that contains comprehensive data (=93 809) on polluting emissions regarding 159 registered chemicals. The data set consists of three database tables. The first table contains a list of industrial entities such as factories. (total: n = 12 173) Each entity comes with a numerical identifier (enti-tyID), as well as its geographical coordinates. The second table provides a list of 160 chemicals, each one having a numerical identifier (chemID). The third table contains a comprehensive list of chemical emissions over a period of 10 years (from 2000 to 2009). Each emission is provided with its date, its entityID and its chemID.
Discussion Comparisons with the SaTScan™ software SaTScan™  [12]  is a software program which implements the spatial scan statistic. It is very widely used (the SaTScan™ user guide  [28]  list over a hundred public health papers which have used it to obtain results in a wide range of studies). Despite its quality and wide user base, SaTScan™ has a certain number of drawbacks which warrant the existence of alternative software tools such as FGBASE. The first drawback is that while SaTScan™ is freely downloadable, it is not open source. This strongly limits its customizability, as the users cannot add and modify features to fit their needs. For example, we (the authors) have benefited from being able to customize the source code of the open source genomic analysis tool Plink  [29]  to add certain statistical tests which we needed but were not implemented in the original software.
A third limitation of SaTScan™ is the lack of cartographic output  [32] , while this can be addressed though the use of an external macro  [32] , an integrated map as the one available in FGBASE, increases the ease of use when viewing clusters. Finally a fourth limitation of SaTScan™, is that it addresses only cluster searches and not the testing of environmental factors. By combining both types of analyses (cluster searches and testing of environmental factors) in a single software, FGBASE facilitates the interpretation of clusters in terms of environmental factors (both clusters and environmental factors are displayed on the same map).
Types of environmental factors handled by FGBASE The "hypothesis driven" grid-based algorithm described in this paper and implemented in the companion software (FGBASE) is well suited in the case of environmental factors, which can be located at specific points. By sampling curves and surfaces as collections of points, these are also well handled. The key aspect is that the environmental factors studied should be of a binary nature, in other words at any given location the factor should either be present or absent. This is the case for factories, power lines, highways and fields of specific crops. This is not the case for factors of a continuous nature such as temperature, atmospheric concentrations of a given gas or particle, or ultraviolet index.