dataesr/scientific-paragraphs-categorization huggingface dataset:
To reduce the rate of ancient DNA errors, treatment with uracil DNA glycosylase and endonuclease VIII (USER mix) is commonly used during library preparation, which generates and cleaves out abasic sites at deaminated cytosines  29. Having a minimized amount of miscoding lesions, UDG-treated libraries, can lead to higher accuracy of aDNA sequences, and are thus more reliable for downstream population genetic analysis. On the other hand, non-UDG-treated libraries are commonly used to verify ancient DNA authenticity through the investigation of aDNA features like DNA fragmentation and the above described nucleotide misincorporation patterns  27. We thus performed shotgun sequencing on both UDG-treated and non-UDG-treated libraries for the whole set of sixty-eight individuals. To assess the ancient origin of DNA sequences, the resulting shotgun sequencing data were aligned against the H.
sapiens reference genome hg38 and postmortem DNA damage signatures evaluated using mapDamage v2. 0. 6. 30. The analysis of damage patterns in the final nucleotide of the sequenced fragments revealed misincorporation frequencies of up to 2. 6% in UDG-treated and up to 21. 9% in non-UDG-treated datasets (Tables  S1  and S2 ). This confirmed that most of the reads mapping to the human reference originate from aDNA fragments. The degree of DNA fragmentation was also explored to further authenticate ancient DNA. The average length of DNA fragments in UDG-treated datasets ranged from 47 to 101 bp (Table  S3 ). In response to the fragmentation and low concentration of endogenous DNA from ancient samples, hybridization capture-based target enrichment can be used to improve the yield of DNA molecules for a specific region of interest.
An HLA target-enrichment approach was thus applied to the UDG-treated libraries, from here on defined as HLA-enriched UDG libraries, to investigate the highly polymorphic classical HLA class I (HLA-A, -B, -C) and class II (HLA-DRB1, -DQB1, -DPB1) genes. To evaluate the performance of the capture approach, endogenous DNA content, fold-enrichments as well as average coverage and read depth over the HLA genes were quantified on both original UDG libraries and HLA-enriched UDG libraries for a subset of 62 samples (library comparison for six samples was not possible because of technical problems unrelated to the data quality). The number of reads mapping to the human reference genome ranged from 56,925 to 137,542,840 when considering the original UDG libraries and from 244,945 to 74,490,774 when considering the HLA-enriched UDG libraries (Table  S4 ).
To call HLA genotypes from the aDNA samples, all the sequence data generated from UDG-treated libraries were combined for each sample and processed through the TARGT pipeline. The TARGT pipeline allows HLA allele identification at up to 3 rd field (6-digit) resolution; however, as most HLA typing tools and HLA genetic studies rely on 2 nd field resolution, we are here only reporting results up to this level. Furthermore, when limited read coverage did not allow for 2 nd field resolution, usually because several alleles of the same 1 st field allele group were equally well supported, the allele call was rounded to that level of resolution (1 st field) (Table  S7 ). Of the 136 alleles (2n) investigated at each locus, we were able to call at 1 st field level 83 alleles for HLA-A, 75 alleles for HLA-B, 74 alleles for HLA-C, 83 alleles for HLA-DRB1, 96 alleles for HLA-DQB1, and 46 alleles for HLA-DPB1.
Of these, the allele call reached the 2 nd field resolution for 45 alleles for HLA-A, 49 alleles for HLA-B, 11 alleles for HLA-C, 58 alleles for HLA-DRB1, 79 alleles for HLA-DQB1, and 46 alleles for HLA-DPB1 (Table  2 ). The success rate calculated across the whole dataset of ancient samples was 56% at the 1 st field level and 35% at 2 nd field level (for values at each locus see Fig. 3  and Table  3 ). As expected, a significant positive correlation between coverage and success rate (1 st field, Kendall τ = 0. 76, p < 0. 001; 2 nd field τ = 0. 76, p < 0. 001; Figure  S3 ) as well as between read depth and success rate (1 st field, τ = 0. 75, p < 0. 001; 2 nd field τ = 0. 75, p < 0. 001; Figure  S4 ) was observed, indicating a considerable effect of 3) from the EAGER pipeline  55. Mapping: performed using Bowtie2 49 against a comprehensive reference file, containing known 3 rd field HLA alleles (following G-group nomenclature).
3. 1, and HLA class I genotype information compared with allele calls from the TARGT pipeline. As OptiType was not designed for low quality data such as usually obtained from aDNA, it has no built-in minimum threshold for data quality, and thus always calls   Table  3. Success rate for the 68 historical samples. Success rate at the 1st field level and at the 2nd field level of resolution, across the 6 investigated genes and overall, for the whole dataset of historical samples. two alleles for a given HLA gene, no matter how spurious the sequence information. For some of the ancient samples, the TARGT approach indicated that low DNA yield and quality, and correspondingly low read coverage, made reliable allele calls impossible at some or all of the investigated HLA genes (Tables  S5  and S7 ).
4 ). Notably, for some samples the resolution of HLA alleles was not possible even at moderate read depth (30×), underlining that allele call success does not depend exclusively on read depth. Indeed, the specific combination of alleles at each genotype can significantly affect the success in calling the HLA alleles. For instance, highly similar alleles that differ only by a few nucleotides would not be resolved even in samples with high read depth, if those few nucleotide positions happen to not be covered by any read. On the other hand, if a particular region that differentiates the two alleles is covered by only a few reads, an accurate allele call might be possible even if the rest of the gene is not covered at all. We further tested if the TARGT pipeline can detect unknown alleles, such as HLA alleles that were present in human history but no longer exist in modern populations, or extremely rare alleles that are not represented in the HLA reference database.
Furthermore, DNA preservation of individual samples can affect allele call success as shown by the strong association between success rate at each sample and both read depth and coverage. Thus, the observed success rate calculated for the historical dataset (56% at 1 st field resolution; 35% at 2 nd field) cannot be generalized for other ancient samples, as it will likely vary depending on the spatial and temporal scales investigated as well as on the degradation of the underlying DNA. The allele calls obtained with the TARGT pipeline were evaluated using an independent approach, the software OptiType  32  , which is currently available only for the typing of HLA class I genes. The comparison revealed a high level of agreement between the two approaches (93%) for the HLA genotyping of class I alleles.
During the evaluation process on simulated aDNA sequences we also observed that the HLA allele calls obtained with the TARGT pipeline achieved high accuracy even in samples whose HLA alleles are not included in the original HLA reference file. These results show that our new approach has the potential to detect unknown alleles, such as extremely rare alleles that are currently absent in the HLA reference database or alleles that were present in the past but no longer exist in modern populations. We then also applied our pipeline to a subset of the 1000 Genomes Project samples in order to test its applicability for shotgun sequence data of modern DNA from population genomic projects. Modern DNA usually exhibits no degradation or extensive fragmentation, thus yielding much longer sequence reads and more even coverage, making allele calling much easier.
Consequently, we observed a much higher success rate (100% at the 1 st field; 90% at the 2 nd field level of resolution) compared with both the empirical and simulated ancient datasets (Table  5 ). These results, together with the high accuracy rate observed (99% at the 1 st field level; 97% at 2 nd field level), suggest that the TARGT pipeline, originally developed to define HLA alleles in aDNA samples, can also be successfully applied to shotgun sequence data from modern DNA. The pipeline includes a pre-processing part with several automated steps essential for the analysis of any next-generation sequencing data: quality control, adapter clipping, and merging of paired reads (Fig. 1 ). This part is optional and can be applied if raw sequence data is to be used for HLA analysis. However, if WGS/WES shotgun data has already been quality-checked and trimmed for other purposes, this can also be used directly as input data for the TARGT pipeline, which would then start with the mapping and sorting steps (Fig.
However, such a stringent threshold might also lead to missing of novel/unknown alleles in a sample, as their specific reads might not map well enough to any known allele and thus would be thrown out. This trade-off should be considered for each given dataset/project, and it might be advisable to run the pipeline multiple times with different thresholds in order to detect the presence of unknown alleles. The default threshold of 1% mismatch balances this trade-off and appears to be a good starting point for most datasets in our experience. The subsequent visual inspection of HLA sequence reads is a critical step for the correct calling of HLA alleles, especially with aDNA samples where miscoding lesions and fragmentation, in combination with the high density of SNPs and paralogous sequences naturally present in the HLA region, can easily lead to incorrect allele calls.
Our results show that the TARGT pipeline is an accurate method for HLA genotyping in case of low-coverage shotgun sequence data. The pipeline also allows for the detection of unknown alleles that are not included in the original HLA reference database. The observed solid performance demonstrates that TARGT is a reliable approach to accurately genotype HLA genes in ancient and modern DNA samples. The pipeline has already been applied successfully to a dataset of medieval European samples, associating HLA variability with susceptibility to leprosy  24  , and indicating its applicability to study the evolution of human resistance or susceptibility to pathogens in historical populations. In addition, the pipeline could be employed to explore HLA allele frequency changes through time, when temporal sample series are available  46  , thus providing a deeper understanding of HLA genetic variation through human history.
To build this reference, nucleotide coding sequences were downloaded from the IMGT/HLA database 9 (accessed 28 July 2015) for the following loci:  HLA-A, -B,-C, -E, -F, -G, -H, -J, -K, -L,  -U , -V, -DQA1, -DQB1, -DRA, -DRB1, -DRB3, -DRB4, -DRB5, -DRB6, -DRB7, -DRB9, -DPA1, -DPB1. Exon sequences of HLA-DQB2 from the human reference genome (not represented in the IMGT/HLA fasta files), was also included, again to preclude misidentification of its reads as HLA-DQB1 variants. Alignment of selected nucleotide sequences was then performed individually for each locus using the program muscle 48 v3. 8. Gaps caused by rare non-functional alleles were removed as well as overhangs upstream and downstream of the exons of interest. Redundant sequence variants that are identical within the exons of interest were also removed (following the G-group nomenclature).
One hundred nucleotides (Ns) were introduced upstream and downstream of all sequences, while 20 Ns were introduced between exon 2 and 3 for class I loci in order to allow for mapping of reads crossing the exon-intron border, as the intron sequences for most alleles are not available from the IMGT/HLA database. All aligned sequences were combined into one FASTA file, which was finally indexed using Bowtie2 to produce the final HLA reference file. Read mapping. The standard input for the TARGT pipeline is qc-filtered and adapter-trimmed short-read sequence data in fastq format. The first step maps the sequence reads of the sample to the HLA reference file using Bowtie2 49 v2. 2. 7 in local alignment mode. Bowtie2 allows mapping of both merged reads and separate paired-end reads. By using the '-a reporting mode' , we allow each read to map against multiple alleles in the reference, which is crucial because of the expected ambiguous mapping of most reads.
23. The HLA capture probes have been originally created considering the full list of available cDNA and gDNA sequences from the IMGT/HLA reference database  9  (i. e. 8,159 alleles), which resulted in a total of 16,351 distinct RNA baits, covering a cumulative target genomic sequence of 215. 5 kb  23. The in-solution targeted capture has been performed using the SureSelectXT Target Enrichment System (Illumina) for the Illumina paired-end multiplexed sequencing library (version B4, August 2015). For each capture reaction, up to four UDG-treated libraries have been pooled. The hybridization reaction required 800 ng of library DNA per pool in a volume of 3. 4 µL. As the UDG-treated libraries were already indexed during library preparation, the 12 cycles of post-capture PCR was performed using 1 µL of each IS5 and IS6 primers (100 µM).
According to the protocol, the resulting amplified captured libraries were purified using the AMPure XP beads, while quality assessment was performed on the Agilent 2100 Bioanalyzer with the High Sensitivity DNA Assay. Finally, sequencing was done on the Illumina HiSeq 4000 (2 × 75 cycles) platform at the Institute of Clinical Molecular Biology, Kiel University, using the HiSeq v4 chemistry and the manufacturer's protocol for multiplex sequencing. Data preprocessing for historical samples. HTS data sets generated for the sixty-eight individuals from St. Jørgen were pre-processed (adapter clipping, merging, trimming) using ClipAndMerge (version 1. 7. 3) from the EAGER pipeline  55. During the adapter clipping step, adapters were excluded when present in the sequence, while reads with fewer than 25 nucleotides after adapter clipping or containing only adapters sequences were removed.
Results of the genome-wide alignment against the human reference were used as input and FASTQ files were generated from aligned BAM files using samtools. OptiType was then applied in DNA mode with default settings. Sequence data from simulated aDnA samples. To validate our HLA genotyping pipeline, we generated simulated aDNA data from genomes with known HLA variants. For this, we first created seven unique MHC haplotypes containing known HLA-B and -DRB1 alleles. The nucleotide sequence of the classical MHC region (chr6: 29,640,000-33,120,000)  61  was downloaded from the UCSC Genome Browser, using the human reference genome GRCh38. The exons forming the variable region in the peptide binding groove (i. e. exon 2 and 3 for the HLA-B locus and exon 2 for the HLA-DRB1 locus) of known alleles were first aligned and then manually edited using BioEdit  50  ; thus creating haplotypes with different alleles from the reference genome.
Additionally, 'de-novo mutations' were introduced in two out of seven haplotypes: in the first haplotype, a point mutation was introduced at each locus (HLA-B and -DRB1); while in the second haplotype a point mutation was introduced only at the HLA-DRB1 locus. The 7 unique HLA haplotypes were then combined in six different diploid combinations of heterozygous genotypes (Table  S14 ). Typical bias observed in aDNA samples (fragmentation and damage patterns) were then introduced using the program gargammel  37. For each genotype, we created five aDNA paired-end read datasets with increasing read depth (1×, 5×, 10×, 30×, 60×), for a total of 30 simulated aDNA samples. In simulating DNA fragmentation, fragment size was calculated considering the average fragment length observed in the set of medieval European samples tested in this study.
Modern sequence data from 1000 Genomes Project. In order to assess the applicability of our pipeline for shotgun sequence data from modern samples, we used whole-exome shotgun sequence data from the 1000 Genomes Project 62. Paired-end sequencing datasets from 31 individuals of diverse ancestry (8 Africans, 8 East Asians, 7 Americans, and 9 Europeans) were downloaded from the 1000 Genomes Project database (phase 3) (Table  S15 ). Only samples with available SBT-based HLA genotype information published in Gourraud et al. 25  were included (Tables  S16  and S17 ). evaluation of HLA allele calling pipeline. To assess the reliability of the TARGT pipeline, three different measures were defined. The success rate quantifies the proportion of cases where an allele call was possible, in both the empirical datasets (historical samples and 1000 Genomes samples) as well as in the simulated aDNA samples.
Acknowledgements This work was supported by the  Max Planck Society , and the  Deutsche Forschungsgemeinschaft (DFG ; grant  LE 2593/3-1  to T. L. L. , grant  2901391021 -SFB 1266/F4  to A. N. and B. K. -K. , and the  DFG Cluster of Excellence "Precision Medicine in Chronic Inflammation" (PMI ,  EXC2167 )). J. B. was funded by the  International Max Planck Research School for Evolutionary Biology. We are grateful to  Jesper L. Boldsen  and  Dorthe Dangvard Pedersen  for access to the St. Jørgen specimens.
Introduction The phenomenon of human migration has been a constant of human history, from the earliest ages until now. As such, the study of migration spans various research fields, including anthropology, sociology, economics, statistics and more recently physics and computer science. We are at a moment where various types of data not typically used to study migration are becoming increasingly available. These include so called social big data: digital traces of humans generated by using mobile phones, online services, online social networks (OSNs), devices within the the internet of things. At the same time, new technologies are able to extract valuable information from these large datasets. Both traditional and novel models and data are currently being employed to understand different questions on migration, including monitoring migration flows, the economic and cultural effects on the migrants and also on the source and destination communities.
In this paper we provide a survey of existing approaches, both traditional and data-rich and, based on the experience of the SoBigData project, we propose new methods and datasets that could contribute significantly to the study of human migration. We concentrate on three different phases of migration: the journey -analysing migration flows and stocks; the stay -studying migrant integration and changes in the communities involved; the returnthe study of migrants returning to the origin country. The journey At the moment, information about migration flows and stocks comes from official statistics obtained either from national censuses or from the population registries. Given that migration intrinsically involves various nations, data is often inconsistent across databases, and offers poor time resolution.
With the availability of social Big Data, we believe it should be possible to estimate flows and stocks from available data in real time, by building models that map observed measures extracted from these unconventional data sources to official data, i. e. nowcasting stocks and flows. We also look at migration phenomena within smaller communities, such as scientific migration, where even prediction of migration events can be possible. An important step in understanding migration flows is suitable visualisation, which we also explore. The stay Migration might generate cultural changes with both long-and short-term effects on the local and incoming population. Migrant integration is generally measured through indicators related to the labour market, economic status or social ties. Again, these statistics are available with low resolution and not for all countries.
Traditional data sources and challenges Tracking international migrants' flows and stocks is an important task but also challenging. At the moment, many researchers and policy makers rely on traditional data sources to study the journey of migrants. Such data sources come from either official statistics or from administrative data. Studying the journey of migrants with these traditional data sources, however, come with various limitations as migration intrinsically involves various nations. For instance, the data are often inconsistent across databases as different countries employ various definitions of a migrant. A lot of efforts have been made so far from both researchers and international organizations to improve quality and harmonize traditional data sources  [53, 150, 130]. International organizations such as the United Nations provide also guidelines and suggestions 1 which countries should employ when dealing with migration statistics.
In this section, each type of data source is described in detail and evaluated. Census data and surveys are official statistics collected by institutions. They provide socio-demographic information of the population, including immigrants. However, the two types of data have different focus. The census data are collected once in five years or once in ten years, depending on the country. For example, the most recent data available in the United States is the 2010 census data, while in Europe the last census was performed in 2011. By the recommendation given by the United Nations 2 , countries should collect the data every year that ends with zero in order to establish a consistency across different migration datasets. But as the process of collecting data is expensive and time consuming, some developing countries do not collect the data as it is recommended, creating inconsistency across different countries' databases.
The high cost is due to the fact that the majority of countries carry out door-todoor or phone interviews to a randomly selected sample of population to collect the data. For instance, the Chinese population is almost 1. 4 billion 3 , so about 6 million enumerators are needed to conduct all the interviews. On the other hand, most European countries retrieve the data from administrative registries which makes the procedure faster  [151, 3]. In the census data, migration related information collected is the following; citizenship, country of birth, last place of residence as well as length of stay. However, depending on the countries' characteristics of immigrants and the immigration system 4 , they do not use the same information to count the number of immigrants. In Europe for example, the focus is also given on different migrant groups depending on whether they are from the European Member States or third country 5.
As a result, there are very few questions related to migration. For instance, in the employment survey in France, there are two questions which are about country of origin and date of arrival. With these two details, it is difficult to infer the immigrants' journey since a clear definition of immigrants cannot be established. As a consequence, it has low accuracy level in capturing immigrants' flows and stocks and real-time observation cannot be done. In addition, information retrieved from surveys refers to a small subset of the entire population. Administrative data are retrieved from registries. It can be from health insurance, residence permits, labour permits or border statistics, which gather also information about immigrants. Registry data can provide more detail and are less costly than official statistics as the information is intrinsically and directly given by the individuals.