dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of a synchrophasor One major difference between PMUs and traditional supervisory control and data acquisition (SCADA) devices usually used for monitoring and control of the electrical grids lays in the synchronization of PMUs to the global positioning system (GPS) time. Doing this way allows the sampled data to be time stamped according to GPS. Data measured by different PMUs at various locations can then be compared to each other for state estimation (static and dynamic behaviour) of the grid. Considering an input waveform represented mathematically by the following equation: ( ) ( ) ( ) t t f X t x m φ π + = 0 2 cos (1) where Xm stands for the magnitude of the sinusoidal waveform, f0 the nominal frequency and φ(t) the instantaneous phase of the input waveform. The phasor representation (figure  2 ) of such a waveform is correlated with the root mean square (rms) value of magnitude and the instantaneous phase as follows: ( ) ( ) t j X X m φ exp 2 = (2) which can be expressed in another manner as : ( ) ( ) t X j t X X i r + = (3) In this representation the instantaneous phase takes into account the possible variation of the grid frequency (the power system seldom operates at exactly the nominal frequency): ( ) 0 2 φ π φ + Δ = ft t (4) which gives for the phasor: ( ) 0 2 exp 2 φ π + Δ = ft j X X m (5) A synchrophasor is then defined as the magnitude and angle of a cosine signal as referenced to an absolute point in time.
Software architecture The software development is made under the LabVIEW Real-Time environment. The real-time operating system is necessary for the determinism (minimum jitter between each iteration) of each step and in order to have an autonomous system. As constructed, the PMU can be used as a stand-alone device capable of storing both raw and processed data or a device communicating with a remote computer, through an Ethernet connection. Figure  3  represents the diagram of the developed system. Some relevant parameters are provided by the operator through the graphical user interface (GUI). These parameters comprise: -the expected frequency of the grid (fn). The value of fn is in close relationship with the appropriate reporting rate (Fs); -the expected magnitude of the input waveform (here is depicted on figure  3  the voltage magnitude associated to one phase, Vn); -the expected phase (here the one associated with one of the voltage phases, phi_vn).
As defined, both acquisition and signal parameters allow, in association with the time-synchronization (phase-lock loop (PLL) with GPS time), to time stamp the acquired samples and generate the theoretical waveform owing to the waveform generator module. The acquired waveform contains information on the data points and the timestamps associated with. Signal processing algorithms developed with Matlab and transcribed thereafter into LabVIEW owing to LabVIEW MathScript module, are used as PMU algorithms to derive the PMU parameters associated with the acquired waveform: frequency (f), amplitude (V), phase (phi) and rate of change of frequency (ROCOF). These quantities are derived for both the synthesized waveform (theoretical synchrophasor) and the acquired waveform (measured synchrophasor).
The TVE calculator algorithm is then used for deriving PMUs errors (TVE, FE and RFE). To this set of data (f, V, phi, ROCOF, TVE, FE and RFE) is associated the timestamp of the middle of the window over which all these calculations are performed. The length of this window being depending on the reporting frame. For example a reporting frame of 10 frames/s (10 Hz for reporting the set of data) corresponds to a maximum window length of 100 ms. And for a total measurement window of one second (Ta = 1 s), the figures mean reporting PMU data set at the maximum rate of 10 times. Another constraint concerns the minimum number of cycles over which the algorithms are performed. For the PMU algorithm, this number is 2 (40 ms for a 50 Hz system). All these constraints have to be taken into account for the selection of the appropriate window over which the calculation process is made (software selection, the only required selection for the operator from the graphical user interface being the window length Ta).
Both Loschbour and Stuttgart likely had dark hair (>99% probability); Loschbour, like La Braña and Motala12, likely had blue or intermediate-colored eyes (>75% probability), while Stuttgart most likely had brown eyes (>99% probability) (SI8). Neither Loschbour nor La Braña carries the skin-lightening allele in SLC24A5 that is homozygous in Stuttgart and nearly fixed in Europeans today, indicating that they probably had darker skin  12. However, Motala12 carries at least one copy of the derived allele, indicating that this locus was already polymorphic in Europeans prior to the advent of agriculture. To place the ancient European genomes in the context of present-day human genetic variation, we assembled a dataset of 2,345 present-day humans from 203 populations genotyped at 594,924 autosomal single nucleotide polymorphisms (SNPs)  5  (SI9) (Extended Data Table  1 ).
Sex Determination and Y-chromosome Analysis We assessed the sex of all sequenced individuals by using the ratio of (chrY) to (chrY+chrX) aligned reads  10. We downloaded a list of Y-chromosome SNPs curated by the International Society of Genetic Genealogy (ISOGG, http://www. isogg. org) v. 9. 22 (accessed Feb. 18, 2014)   and determined the state of the ancient individuals at positions where a single allele was observed and MAPQ≥30. We excluded C/G or A/T SNPs due to uncertainty about the polarity of the mutation in the database. The ancient individuals were assigned haplogroups based on their derived state (SI5). We also used BEAST v1. 7. 51  61  to assess the phylogenetic position of Loschbour using 623 males from around the world with 2,799 variant sites across 500kb of nonrecombining Y-chromosome sequence 62 (SI5).
Human Origins dataset curation The Human Origins array consists of 14 panels of SNPs for which the ascertainment is well known  5, 70. All population genetics analysis were carried out on a set of 594,924 autosomal SNPs, after restricting to sites that had >90% completeness across 7 different batches of sequencing, and that had >97. 5% concordance with at least one of two subsets of samples for which whole genome sequencing data was also available. The total dataset consists of 2,722 individuals, which we filtered to 2,345 individuals (203 populations) after removing outlier individuals or relatives based on visual inspection of PCA plots  15, 71  or model-based clustering analysis  14. Whole genome amplified (WGA) individuals were not used in analysis, except for a Saami individual who we forced in because of the special interest of this population for Northeastern European population history (Extended Data Fig.
ADMIXTURE analysis We merged all Human Origins genotype data with whole genome sequencing data from Loschbour, Stuttgart, MA1, Motala12, Motala_merge, and LaBrana. We then thinned the resulting dataset to remove SNPs in linkage-disequilibrium with PLINK 1. 07  72  , using a window size of 200 SNPs advanced by 25 SNPs and an r 2 threshold of 0. 4. We ran ADMIXTURE 1. 23  14, 73  for 100 replicates with different starting random seeds, default 5-fold cross-validation, and varying the number of ancestral populations K between 2 and 20. We assessed clustering quality using CLUMPP  74. We used the ADMIXTURE results to identify a set of 59 "West Eurasian" (European/Near Eastern) populations based on values of a "West Eurasian" ancestral population at K=3 (SI9). We also identified 15 populations for use as "non-West Eurasian outgroups" based on their having at least 10 individuals and no evidence of European or Near Eastern admixture at K=11, the lowest K for which Near Eastern/European-maximized ancestral populations appeared consistently across all 100 replicates.
f 3 -statistics We use the f 3 -statistic 5  3 (;  1 ,  2 ) =   21  and a block size of 5cM. We report significance as the number of standard errors by which the statistic differs from zero (Z-score). We also perform an analysis in which we constrain the reference populations to be (i) EEF (Stuttgart) and WHG (Loschbour or LaBrana), (ii) EEF and a Near Eastern population, (iii) EEF and ANE (MA1), or (iv) any two present-day populations, and compute a Z diff score between the lowest f 3 -statistic observed in the dataset, and the f 3 -statistic observed for the specified pair. relatedness to ancient populations (Extended Data Fig. 6 ). We computed D-statistics  63  using transversion polymorphisms in whole genome sequence data  35  to confirm robustness to ascertainment and ancient DNA damage (Extended Data Table  2 ).
TreeMix analysis We applied TreeMix 34 to Loschbour, Stuttgart, Motala12, and MA1 6 , LaBrana  12  and the Iceman  19  , along with the present-day samples of Karitiana, Onge and Mbuti. We restricted the analysis to 265,521 Human Origins array sites after excluding any SNPs where there were nocalls in any of the studied individuals. The tree was rooted with Mbuti and standard errors were estimated using blocks of 500 SNPs. We repeated the analysis on whole-genome sequence data, rooting with Chimp and replacing Onge with Dai since we did not have Onge whole genome sequence data  35. We varied the number of migration events (m) between 0 and 5.
Haplotype-based analyses We used RefinedIBD from BEAGLE 4  77  with the settings ibdtrim=20 and ibdwindow=25 to study IBD sharing between Loschbour and Stuttgart and populations from the POPRES dataset  42. We kept all IBD tracts spanning at least 0. 5 centimorgans (cM) and with a LOD score >3 (SI18). We also used ChromoPainter  37  to study haplotype sharing between Loschbour and Stuttgart and present-day West Eurasian populations (SI19). We identified 495,357 SNPs that were complete in all individuals and phased the data using Beagle 4  77  with parameters phase-its=50 and impute-its=10. We did not keep sites with missing data to avoid imputing modern alleles into the ancient individuals. We combined ChromoPainter output for chromosomes 1-22 using ChromoCombine  37. We carried out a PCA of the co-ancestry matrix using fineSTRUCTURE  37.
Motala The Motala samples come from the site of Kanaljorden in the town of Motala, Östergötland, Sweden. The site was excavated between 2009 and 2013. The samples that we analyzed in the present study were retrieved in and 2010 (Extended Data Figs. 1C, 1D ). The human remains are part of ritual depositions that were made on a 14 × 14 meter stone-packing, constructed on the bottom of a small lake. The stone-packing was completely submerged and covered by at least 0. 5m of water at the time of use. The ritual depositions include human bones: mostly skulls and fragments of skulls but also some stray bones from other parts of the body. The minimal number of individuals is inferred to be ten adults and one infant. The infant is the only individual that has bone representation from the entire body.
Overview This note describes the processing of the sequence data for the Loschbour, Stuttgart and Motala samples. It also describes the estimation of heterozygosity for the high coverage Stuttgart and Loschbour individuals. For Stuttgart, heterozygosity was estimated to be higher than in any of 15 present-day non-African samples and lower than any of 10 present-day African samples. For Loschbour, heterozygosity was estimated to be lower than in any of 25 present-day samples.
Sequencing data All ancient DNA (aDNA) libraries were sequenced on the Illumina HiSeq platform. Base-calling was carried out using the default Illumina basecaller, Bustard, except where noted. The following data were generated (summarized in Table  S2. 1): Loschbour: 1. Four double-stranded libraries (ALB11-14) were sequenced for 101-cycles, paired-end, on a HiSeq 2500 platform (8 lanes). Base-calling was performed using freeIbis 1. 2. Nine single-stranded libraries (ALB2-10) were sequenced for 100-cycles, paired-end, on a HiSeq 2000 platform. This consisted of 3 lanes for ALB2 and 5 lanes for a pool of ALB3-10.
The seven Motala samples had to be treated slightly differently. Initial light shotgun sequencing of seven Motala libraries was performed to determine candidate libraries for deeper sequencing. The samples were sequenced as a pool, so we de-multiplexed the data by searching among the sequences for ones that had no more than one mismatch compared with each of the expected P7 and P5 indices for the seven samples. Reads were stripped of adapters, merged using SeqPrep  7  , and aligned with BWA 3 version 0. 5. 10, with parameters "-n 0. 01 -o 2" (seed disabled). Duplicates were removed using samtools 5 0. 1. 18. PCA indicated that the Motala samples were relatively homogenous in ancestry and we therefore merged the data for all of the samples except for Motala3 and Motala12 (using samtools merge5) to increase coverage for population genetic analysis (labeled 'Motala_merge' in Fig.
Estimation of heterozygosity Two tools were used to estimate heterozygosity: 1. mlRho 9 , which estimates heterozygosity as the maximum likelihood of the population mutation SDUDPHWHU ș IURP KLJK-coverage data of one individual, assuming an infinite sites model of mutation 7KH SURJUDP DOVR HVWLPDWHV WKH VHTXHQFLQJ HUURU UDWH SHU VLWH İ 2. GATK. The GATK genotype calls are viewed as correct, and the number of called heterozygous sites divided by the total number of screened nucleotides is interpreted as the heterozygosity. Heterozygosity was estimated in the high-coverage genome sequences from 29 individuals: 25 diverse present-day humans, Altai Neandertal, Denisova, Stuttgart and Loschbour. This is the same dataset previously described on the high coverage Neandertal genome, here supplemented by Stuttgart and Loschbour  10.
The mtDNA contamination rates for the deeply sequenced shotgun data from UDG-treated libraries were estimated by direct comparison to the mtDNA consensus from the targeted mtDNA enrichment. No mtDNA capture was performed for Stuttgart. For this sample, deep sequencing data was used to analyze DNA damage patterns from a non-UDG-treated library (ALBK1). The mtDNA contamination estimate was obtained from high coverage shotgun data from a UDG-treated library (ALBK2).
Presence of aDNA-typical C-to-T damage patterns at the 5'-ends of DNA fragments The percentage of C-to-T changes at the 5'-ends of endogenous mtDNA fragments from libraries that were non-UDG-treated was estimated using mapDamage2. 0 7 (the exception is Stuttgart (ALBK1), where nuclear DNA from shotgun data was used to estimate damage patterns). Figure  S3. 1 shows the difference in DNA damage patterns of DNA mapping to the human genome (hg19) for libraries from Loschbour and Stuttgart with and without UDGtreatment. Based on previous evidence that samples older than 100 years typically have at least 20% deamination at the 5' ends  8  , only samples that show more than 20% damage were considered as good candidates for harboring authentic ancient DNA (Table  S3. 1). All 8 samples that show internally consistent mtDNA show more than 20% damage at the 5'-ends and therefore meet our criteria of aDNA authenticity for further processing.
Absence of polymorphic sites on chromosome X As Loschbour is very likely a male (SI5), heterozygous sites along the X chromosome are not expected. Sites where a second allele is observed are then due to: 1. Contamination 2. Sequencing errors 3. Mismapping In an approach similar to that used for the Australian Aboriginal Genome 3 , we computed the frequency of each base at positions that are polymorphic on chromosome X in the 1000 Genomes 10 dataset. To reduce the effect of mismapping, only genomic regions with high mappability (SI2) were analyzed. Reads were required to have a mapping quality of at least 30, and only bases with a quality of at least 30 were considered for this analysis. Sites were required to fall within the 95 th percentile of the coverage distribution for chromosome X, resulting in a minimum coverage of 4× and a maximum of 21×.
Phylogenetic analysis of the mitochondrial genomes The consensus sequences of all samples that fulfilled the authenticity criteria were assigned to haplogroups using HaploFind 7 (Table  S4. 1). All Mesolithic genomes belong to haplogroups U2 or U5, which are common among pre-Neolithic Europeans as has been shown earlier 2, 3, 8-14 (Figure  S4. 1). Motala 2 and 12 share the same haplotype, suggesting a close relationship through the maternal lineage. The Neolithic sample Stuttgart belongs to haplogroup T2, which is common among early European farmers  8, 11, [14] [15] [16] [17] [18] [19] [20] [21]  as well as present-day Europeans 22. The mtDNA consensus sequences were aligned using the software MUSCLE  23. MEGA 5. 2  24  was used to generate a Maximum Parsimony tree, which included the mtDNA sequences obtained here along with previously published complete early modern human mtDNAs 2, 3, 12, 14, 25-28 and 54 presentday human mtDNAs from a worldwide dataset 29.
Sex determination and Y chromosome analysis Iosif Lazaridis † and Gabriel Renaud †* * To whom correspondence should be addressed (gabriel_renaud@eva. mpg. de) † Contributed equally to this section We infer which of the ancient human individuals in this study are likely to be male, and determine their Y chromosome haplogroups using publicly available Y chromosome SNPs. For Loschbour, which has the highest coverage of all the samples, we also determine its phylogenetic placement in a larger Y-SNP dataset of present-day humans.
Phylogenetic analysis of the Loschbour Y chromosome Given that Loschbour carried a Y chromosome belonging to haplogroup I, we sought to investigate how this individual's Y-chromosome compares to the diversity of present-day humans. We used a dataset from Lippold et al. 3  which contains the genotype at 2,799 positions for a worldwide panel of 623 Y chromosomes. Using BEAST v1. 7. 51  4  with a coalescence prior of 60,000 years for all non-Africans and a tip age for Loschbour of 8,000 years, we reconstructed a Bayesian inference tree. This analysis makes no assumptions about the phylogeny, or about the Y chromosome mutation rate. Instead, the phylogeny is reconstructed based on Y chromosome polymorphism data from the analyzed samples themselves and the mutation rate is inferred based on "branch shortening"; the rate of missing mutations on the Loschbour-specific lineage due to the fact that it has evolved less.
Introduction We assessed three ancient modern humans (the Loschbour forager, the Motala12 forager, and the Stuttgart farmer) at a panel of SNPs and multi-allelic markers having well-validated phenotypic effects in present-day humans. Many of the markers in our panel have also been affected by natural selection relatively recently in human prehistory. Walsh et al. [1] [2] [3] [4]  , among others  [5] [6] [7]  , demonstrated that it is possible to predict human eye, hair, and skin color phenotypes with accuracy using a small number of DNA variants. Here, we predicted pigmentation phenotypes of the three ancient modern humans using two models 2,3,6-8 that have been validated in present-day populations 1,4,7-9 as well as on skeletal remains  10. We used these models together with additional SNP and haplotype data to infer the most likely iris, hair and skin pigmentation for the Loschbour, Motala12, and Stuttgart individuals.
Methods We analyzed DNA polymorphism data stored in the VCF format  20  using the VCFtools software package (http://vcftoools. sourceforge. net/). For the Loschbour and Stuttgart individuals, we included data from VLWHV QRW IODJJHG DV /RZ4XDOLW\ ZLWK JHQRW\SH TXDOLW\ *4 RI DQG 613 TXDOLty 48$/ RI. We chose to assess Motala12 because this individual was sequenced at higher average coverage than the other Motala samples. However, the coverage of the Motala12 individual (2. 4×) was appreciably lower than that of the Loschbour (22×) and Stuttgart (19×) individuals, and we therefore altered our genotyping methodology to account for the limitations imposed by lower coverage at our sites of interest. Specifically, we included sites having at least 2× coverage which passed visual inspection of the local alignment using samtools tview (http://samtools.
differences in genotyping results for each batch that have nothing to do with population history. Moreover, a subset of samples were from whole genome amplified (WGA) material rather than from genomic DNA extracted from blood and saliva, and we were concerned that these samples might have systematic differences from the other samples. Populations comprised of samples derived from WGA material are identified with the suffix "_WGA" in the dataset that we are making available. To curate the data, we began by computing the following statistics on each SNP: (1) Genotyping concordance over 69 samples from the West African Yoruba (YRI) that overlapped between this study and low coverage sequencing data from the 1000 Genomes Project 5. (2) Genotyping concordance rate over 25 samples from diverse populations that overlapped between this study and high coverage genome sequences reported in ref.
6. ( (5) Homozygous, Heterozygous, and Variant genotype counts for a pool of West Africans and a pool of Europeans over all batches but excluding WGA samples. We include only females from chromosome X SNPs so that all genotypes are diploid. (6) Male and female frequencies for a pool of West Africans and a pool of Europeans over all batches excluding WGA samples. We first computed the following statistics to filter out potentially problematic SNPs. • "Maxconcordance" -Maximum concordance with either the 69 1000 Genomes Project 5 or 25 deeply sequenced  6  samples. If a site has missing data in a sequencing dataset the concordance is reported as 0 for that dataset. • "Completeness" -Completeness percentage of genotyping across the WGA samples. • "Mincompleteness" -Minimum completeness percentage for the SNP across 7 genotyping batches.
• "WGAcompleteness" -Completeness for SNP in the WGA data. Table  S9. 1 gives the fraction of autosomal SNPs that would be retained after applying different thresholds (for all but the WGAcompleteness metric). We also pooled all non-whole genome amplified (non-WGA) West Eurasians and all non-WGA West Africans. This gave us counts of the three possible genotypes for each SNP: homozygous reference, Table  S9. 2 shows the filters we chose for the dataset. For the analyses reported in this study, we restrict to non-WGA samples with the exception of the Saami individual which we force in because of its high interest, and use thresholds that strike a balance between retaining a large fraction of SNPs while removing extreme outliers. For users who wish to analyze the data from WGA samples which have a higher missing data rate than the other samples and where the missing data is concentrated disproportionally at particular SNPs, we recommend imposing the stronger thresholds.
Filtering of samples A total of 2,722 samples were successfully genotyped. A total of 2,395 samples remained after outlier removal. For outlier removal, we manually curated the data using ADMIXTURE  7  and EIGENSOFT  8, 9  to identify samples that were visual outliers compared with samples from their own populations. We also identified samples that were apparently closely relatives of others in the dataset. In the dataset that we release, the "verbose" population IDs for these individuals are prefixed by the string "Ignore_", so that users who wish to analyze these samples are still able to analyzed the data. A total of 2,345 individuals were used in our analysis dataset, after further omitting all samples that were genotyped from whole genome amplified (WGA) material, with the exception of the Saami.
This results in 203 populations with at least 1 sample each in our analysis dataset (Table  S9. 3). A total of 2,244 samples (of the 2,722) are in a version of the dataset we have made freely available at http://genetics. med. harvard. edu/reichlab/Reich_Lab/Datasets. html. The remaining 478 samples have more restrictive procedures for data access, and users who wish to access the data will need to send the corresponding author (DR) a signed letter containing the text shown in Box S9. 1.