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  • European Marine Science
  • Open Access
  • European Commission
  • Wellcome Trust
  • EC|FP7
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  • European Marine Science

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Leathlobhair, Máire Ní; Perri, Angela R.; Irving-Pease, Evan K.; Witt, Kelsey E.; +46 Authors

    Dogs were present in the Americas prior to the arrival of European colonists, but the origin and fate of these pre-contact dogs are largely unknown. We sequenced 71 mitochondrial and seven nuclear genomes from ancient North American and Siberian dogs spanning ~9,000 years. Our analysis indicates that American dogs were not domesticated from North American wolves. Instead, American dogs form a monophyletic lineage that likely originated in Siberia and dispersed into the Americas alongside people. After the arrival of Europeans, native American dogs almost completely disappeared, leaving a minimal genetic legacy in modern dog populations. Remarkably, the closest detectable extant lineage to pre-contact American dogs is the canine transmissible venereal tumor, a contagious cancer clone derived from an individual dog that lived up to 8,000 years ago. Mitochondrial DNA FASTA fileFASTA file containing 1166 dog mtDNA genomes used in this studyfull_mtDNA_alignment.fastaNEXUS treeMaximum likelihood tree (RAxML) of 1166 dogs mtDNA genomes used in this studyfull_mtDNA_alignment.treExcel sheetPublication source of the 1166 mtDNA genomes used in this studyfull_mtDNA_alignment.xlsxPlink (bed) fileContains genotype for dogs 54 dogsfull_data.bedPlink file (bim)Contains genotype for 54 dogsfull_data.bimPlink file (fam)Contains genotype for 54 dogsfull_data.famNJ tree in Figure 2bNJ tree in Figure 2b (see Table S2 for more info)Figure_b.treNexus fileNexus file used for producing Figure S12 (MKV model in MrBayes)Binary_char_MKV.nexNEXUS treeBayesian tree in Figure S12 (see Table S2 for more info)Figure_S12.tre

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; NARCISarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DRYAD; NARCIS
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; NARCIS
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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
    Borealis
    Dataset . 2021
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; NARCISarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DRYAD; NARCIS
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; NARCIS
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
      Borealis
      Dataset . 2021
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Soria-Carrasco, Victor; Gompert, Zachariah; Comeault, Aaron A.; Farkas, Timothy E.; +9 Authors

    Natural selection can drive the repeated evolution of reproductive isolation, but the genomic basis of parallel speciation remains poorly understood. We analyzed whole-genome divergence between replicate pairs of stick insect populations that are adapted to different host plants and undergoing parallel speciation. We found thousands of modest-sized genomic regions of accentuated divergence between populations, most of which are unique to individual population pairs. We also detected parallel genomic divergence across population pairs involving an excess of coding genes with specific molecular functions. Regions of parallel genomic divergence in nature exhibited exceptional allele frequency changes between hosts in a field transplant experiment. The results advance understanding of biological diversification by providing convergent observational and experimental evidence for selection’s role in driving repeatable genomic divergence. bgsr.tarC++ source code to analyze the transplant experimetns. This program analyzes allele frequency changes in experimental populations. The model assumes a set of experimental populations were founded from a single source population and that a sample of individuals from the source population exist. Further, the model assumes that individuals are sampled from each experimental population after a single generation. This software requires the GSL and HDF5. To compile the software try: h5c++ -O3 -o bgsr main.C mcmc.C func.C -lm -lgsl -lgslcblasfstanalysisR code to calculate SNP-specific Fst.fitHmmR code to fit and summarize the HMM for divergence state based on SNP Fst estimates.annotationScriptsThis compressed directory contains perl scripts to retrieve gene annotation information for focal loci, an R script for an annotation-based randomization test, example files, and a README file.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DRYAD; NARCIS
    Dataset . 2015
    License: CC 0
    Data sources: Datacite; NARCIS
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2014
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2015
      License: CC 0
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DRYAD; NARCIS
      Dataset . 2015
      License: CC 0
      Data sources: Datacite; NARCIS
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2014
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Radersma, Reinder; Garroway, Colin J.; Santure, Anna W.; De Cauwer, Isabelle; +3 Authors

    Individual genotypes (SNP-markers)Genotypes (SNPs markers) of all individuals in this study. Columns are SNP markers and rows are individuals. This matrix contains a header with SNP-marker names. Identities of the individuals can be found in the "list of individuals in the genotype matrix".SNPs.csvList of individuals in the genotype matrixList of the identities of all individuals in the "individual genotypes" matrix. Each entry corresponds to a row in the "individual genotypes" matrix.list_of_individuals_SNPs.csvList of candidate genesList of candidate genes. This file contains a header. marker_name corresponds to the marker identities (columns) in the " individual genotypes" matrix. gene_cat are the categories investigated in this study. candidate_gene are the corresponding gene names.list_of_candidate_genes.csvIndividual by gathering event matrix 2007This matrix indicates which individuals were present at which gathering events. Columns are gathering events, rows are individuals. Gathering events are ordered by time and date (from early to late). Presence at a gathering event is marked with a 1. The identities of the individuals can be found in the "list of individuals for the I by GE matrix 2007". The locations of the gathering events can be found in the "list of locations of the gathering events 2007".individual_by_gathering_event_2007.csvList of individuals for the I by GE matrix 2007List of the identities of the individuals in the "individual by gathering event matrix 2007". Each entry corresponds to a row in the "individual by gathering event matrix 2007".list_of_individuals_2007.csvList of locations of the gathering events 2007List of the locations of all gathering events in 2007. Each entry corresponds to a column in the "individual by gathering event matrix 2007". The values correspond to the rows and columns in the "spatial distance matrix" and the rows in the "eigen vectors of the spatial weighting matrix".list_of_locations_of_gathering_events_2007.csvIndividual by gathering event matrix 2008This matrix indicates which individuals were present at which gathering events. Columns are gathering events, rows are individuals. Gathering events are ordered by time and date (from early to late). Presence at a gathering event is marked with a 1. The identities of the individuals can be found in the "list of individuals for the I by GE matrix 2008". The locations of the gathering events can be found in the "list of locations of the gathering events 2008".individual_by_gathering_event_2008.csvList of individuals for the I by GE matrix 2008List of the identities of the individuals in the "individual by gathering event matrix 2008". Each entry corresponds to a row in the "individual by gathering event matrix 2008".list_of_individuals_2008.csvList of locations of the gathering events 2008List of the locations of all gathering events in 2008. Each entry corresponds to a column in the "individual by gathering event matrix 2008". The values correspond to the rows and columns in the "spatial distance matrix" and the rows in the "eigen vectors of the spatial weighting matrix".list_of_locations_of_gathering_events_2008.csvIndividual by gathering event matrix 2009This matrix indicates which individuals were present at which gathering events. Columns are gathering events, rows are individuals. Gathering events are ordered by time and date (from early to late). Presence at a gathering event is marked with a 1. The identities of the individuals can be found in the "list of individuals for the I by GE matrix 2009". The locations of the gathering events can be found in the "list of locations of the gathering events 2009".individual_by_gathering_event_2009.csvList of individuals for the I by GE matrix 2009List of the identities of the individuals in the "individual by gathering event matrix 2009". Each entry corresponds to a row in the "individual by gathering event matrix 2009".list_of_individuals_2009.csvList of locations of the gathering events 2009List of the locations of all gathering events in 2009. Each entry corresponds to a column in the "individual by gathering event matrix 2009". The values correspond to the rows and columns in the "spatial distance matrix" and the rows in the "eigen vectors of the spatial weighting matrix".list_of_locations_of_gathering_events_2009.csvSpatial distance matrixThe spatial distances between all feeding stations (in meters). Rows and columns correspond to the locations of the feeding stations, i.e. the values in the "list of locations of gathering events 2007/2008/2009" files.spatial_distance_matrix.csvDistance based Moran's Eigenvector MapsThe distance based Moran's Eigenvector Maps (db-MEMs) for all feeding stations. Rows correspond to the feeding station locations and columns to the 20 Eigenvectors.spatial_weighting_matrix_Eigen_vectors.csvAsymmetric Eigenvector Maps 2007The Asymmetric Eigenvector Maps for the gathering events in 2007. Rows correspond to the gathering events and columns to the first 500 Eigenvectors.asymmetric_eigenvector_maps_2007.csv.zipAsymmetric Eigenvector Maps 2008The Asymmetric Eigenvector Maps for the gathering events in 2008. Rows correspond to the gathering events and columns to the first 500 Eigenvectors.asymmetric_eigenvector_maps_2008.csv.zipAsymmetric Eigenvector Maps 2009The Asymmetric Eigenvector Maps for the gathering events in 2009. Rows correspond to the gathering events and columns to the first 500 Eigenvectors.asymmetric_eigenvector_maps_2009.csv.zip Social interactions are rarely random. In some instances animals exhibit homophily or heterophily, the tendency to interact with similar or dissimilar conspecifics respectively. Genetic homophily and heterophily influence the evolutionary dynamics of populations, because they potentially affect sexual and social selection. Here we investigate the link between social interactions and allele frequencies in foraging flocks of great tits (Parus major) over three consecutive years. We constructed co-occurrence networks which explicitly described the splitting and merging of 85,602 flocks through time (fission-fusion dynamics), at 60 feeding sites. Of the 1711 birds in those flocks we genotyped 962 individuals at 4701 autosomal single-nucleotide polymorphisms (SNPs). By combining genome-wide genotyping with repeated field observations of the same individuals we were able to investigate links between social structure and allele frequencies at a much finer scale than was previously possible. We explicitly accounted for potential spatial effects underlying genetic structure at the population level. We modelled social structure and spatial configuration of great tit fission-fusion dynamics with eigenvector maps. Variance partitioning revealed that allele frequencies were strongly affected by group fidelity (explaining 27-45% of variance) as individuals tended to maintain associations with the same conspecifics. These conspecifics were genetically more dissimilar than expected, shown by genome-wide heterophily for pure social (i.e. space-independent) grouping preferences. Genome-wide homophily was linked to spatial configuration, indicating spatial segregation of genotypes. We did not find evidence for homophily or heterophily for putative socially relevant candidate genes or any other SNP markers. Together, these results demonstrate the importance of distinguishing social and spatial processes in determining population structure.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2017
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DRYAD; NARCIS
    Dataset . 2017
    License: CC 0
    Data sources: Datacite; NARCIS
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2017
    License: CC 0
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DANS-EASYarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2017
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DRYAD; NARCIS
      Dataset . 2017
      License: CC 0
      Data sources: Datacite; NARCIS
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2017
      License: CC 0
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lewis, Nicola S.; Russell, Colin A.; Anderson, Tavis K.; Berger, Kathryn; +26 Authors

    Figure 1. Source data 1Figure 1. Source data 1 - Input and output text files containing all H1 and H3 virus data used in Figure 1, Figure1 Supplemental 1,2,3, filesFigure1.Source data 1.zipFigure 2. Source data 2Figure 2. Source data 2 - Input and output text files used to calculate H1 and H3 comparative rates of antigenic evolution and diversity shown in Figure 2 and Supplementary File 3_ Table 3Figure2.Source data 2.zipSupplemental Table 1. Source DataSupplemental Table 1. Source Data - Text files and plots of summary statistics of testing in 1, 2, 3, 4 and 5 dimensionsSupplemental Table1.Source Data.zipSupplemental Table 2. Source dataSupplemental Table 2.Source data Text infiles, phylogenetic trees and source script for antigenic distance analysesSupplemental Table 2.Source data.zipFigure 1Agif files for rotational views of 3D antigenic maps in Figure 1Figure1A.H1_lineages.gifFigure 1Bgif files for rotational views of 3D antigenic maps in Figure 1Figure1B.H1_regions.gifFigure 1Cgif files for rotational views of 3D antigenic maps in Figure 1Figure1C.H3_lineages.gifFigure 1Dgif files for rotational views of 3D antigenic maps in Figure 1Figure1D.H3_regions.gif Swine influenza presents a substantial disease burden for pig populations worldwide and poses a potential pandemic threat to humans. There is considerable diversity in both H1 and H3 influenza viruses circulating in swine due to the frequent introductions of viruses from humans and birds coupled with geographic segregation of global swine populations. Much of this diversity is characterized genetically but the antigenic diversity of these viruses is poorly understood. Critically, the antigenic diversity shapes the risk profile of swine influenza viruses in terms of their epizootic and pandemic potential. Here, using the most comprehensive set of swine influenza virus antigenic data compiled to date, we quantify the antigenic diversity of swine influenza viruses on a multi-continental scale. The substantial antigenic diversity of recently circulating viruses in different parts of the world adds complexity to the risk profiles for the movement of swine and the potential for swine-derived infections in humans.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2017
    License: CC 0
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2016
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DRYAD; NARCIS
    Dataset . 2017
    License: CC 0
    Data sources: Datacite; NARCIS
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      ZENODO
      Dataset . 2017
      License: CC 0
      Data sources: ZENODO
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      DANS-EASY
      Dataset . 2016
      Data sources: B2FIND
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      DRYAD; NARCIS
      Dataset . 2017
      License: CC 0
      Data sources: Datacite; NARCIS
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    Authors: Langat, Pinky; Raghwani, Jayna; Dudas, Gytis; Bowden, Thomas A.; +10 Authors

    Full genomes location summaryTab-delimited text file detailing locations and lineages of 2,651 full-length influenza B virus genomes in this study (Fig 1 source data).fluB_location_summary.txtFull genomes time summaryTab-delimited text file detailing year of isolation and lineage of 2,651 full-length influenza B virus genomes from this study (Fig 1 source data).fluB_timeseries_summary.txtMaximum likelihood treesCompressed file of bootstrapped ML phylogenies inferred using RAxML and used to characterise influenza B virus genotypes.maximum-likelihood-trees.zipBEAST XML filesCompressed file of BEAST input files (xml) used to infer the molecular clock phylogenies as well as ancestral reconstruction and phylodynamic analysis for influenza B virus genes.beast-xml-files.zipMaximum clade credibility (MCC) treesCompressed file comprising of BEAST output MCC trees.mcc-trees.zipSummary of genotypesTab-delimited text file detailing inferred genotypes for influenza B viruses, representing over 10,000 strains including 2,651 complete genomes.genotypes-summary.txtMCC trees annotated with trunk substitutionsCompressed file containing MCC trees for B/Yamagata HA, B/Victoria HA, B/Yamagata PB1, and B/Victoria NA annotated with inferred mutations along the trunk lineages.trunk-substitutions-mcc-trees.zipInput files for antigenic analysisCompressed file comprising of: tab-delimited HI data file, BEAST XML file for generation of set of empirical trees and XML file for running BMDS models for B/Yamagata and B/Victoria.antigenic-evolution-input.zipOutput files from antigenic analysisCompressed file containing MCC trees from BMDS analysis (source data for Figure 4) and tab-delimited file summaries of mean antigenic distance from phylogenetic root inferred across 2,000 posterior trees to calculate mean antigenic drift rates (source data for Table 2). Posterior trees files were too large (>50 MB in compressed form each) to be included.antigenic-evolution-output.zip The global-scale epidemiology and genome-wide evolutionary dynamics of influenza B remain poorly understood compared with influenza A viruses. We compiled a spatio-temporally comprehensive dataset of influenza B viruses, comprising over 2,500 genomes sampled worldwide between 1987 and 2015, including 382 newly-sequenced genomes that fill substantial gaps in previous molecular surveillance studies. Our contributed data increase the number of available influenza B virus genomes in Europe, Africa and Central Asia, improving the global context to study influenza B viruses. We reveal Yamagata-lineage diversity results from co-circulation of two antigenically-distinct groups that also segregate genetically across the entire genome, without evidence of intra-lineage reassortment. In contrast, Victoria-lineage diversity stems from geographic segregation of different genetic clades, with variability in the degree of geographic spread among clades. Differences between the lineages are reflected in their antigenic dynamics, as Yamagata-lineage viruses show alternating dominance between antigenic groups, while Victoria-lineage viruses show antigenic drift of a single lineage. Structural mapping of amino acid substitutions on trunk branches of influenza B gene phylogenies further supports these antigenic differences and highlights two potential mechanisms of adaptation for polymerase activity. Our study provides new insights into the epidemiological and molecular processes shaping influenza B virus evolution globally.

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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    ZENODO
    Dataset . 2018
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      DRYAD; NARCIS
      Dataset . 2018
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      DANS-EASY
      Dataset . 2018
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      ZENODO
      Dataset . 2018
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    Authors: Pinharanda, A.; Martin, S. H.; Barker, S. L.; Davey, J. W.; +1 Authors

    Gene duplications can facilitate adaptation and may lead to interpopulation divergence, causing reproductive isolation. We used whole-genome resequencing data from 34 butterflies to detect duplications in two Heliconius species, Heliconius cydno and Heliconius melpomene. Taking advantage of three distinctive signals of duplication in short-read sequencing data, we identified 744 duplicated loci in H. cydno and H. melpomene and evaluated the accuracy of our approach using single-molecule sequencing. We have found that duplications overlap genes significantly less than expected at random in H. melpomene, consistent with the action of background selection against duplicates in functional regions of the genome. Duplicate loci that are highly differentiated between H. melpomene and H. cydno map to four different chromosomes. Four duplications were identified with a strong signal of divergent selection, including an odorant binding protein and another in close proximity with a known wing colour pattern locus that differs between the two species.

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    Apollo
    Article . 2017
    License: CC BY
    Data sources: Datacite
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    Apollo
    Other literature type . 2017
    License: CC BY
    Data sources: Apollo
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      Apollo
      Article . 2017
      License: CC BY
      Data sources: Datacite
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      Apollo
      Other literature type . 2017
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    Authors: Key, Timothy J; Appleby, Paul N; Bradbury, Kathryn E; Sweeting, Michael; +51 Authors

    Background: There is uncertainty about the relevance of animal foods to the pathogenesis of ischemic heart disease (IHD). We examined meat, fish, dairy products, and eggs and risk for IHD in the pan-European EPIC cohort (European Prospective Investigation Into Cancer and Nutrition). Methods: In this prospective study of 409 885 men and women in 9 European countries, diet was assessed with validated questionnaires and calibrated with 24-hour recalls. Lipids and blood pressure were measured in a subsample. During a mean of 12.6 years of follow-up, 7198 participants had a myocardial infarction or died of IHD. The relationships of animal foods with risk were examined with Cox regression with adjustment for other animal foods and relevant covariates. Results: The hazard ratio (HR) for IHD was 1.19 (95% CI, 1.06–1.33) for a 100-g/d increment in intake of red and processed meat, and this remained significant after exclusion of the first 4 years of follow-up (HR, 1.25 [95% CI, 1.09–1.42]). Risk was inversely associated with intakes of yogurt (HR, 0.93 [95% CI, 0.89–0.98] per 100-g/d increment), cheese (HR, 0.92 [95% CI, 0.86–0.98] per 30-g/d increment), and eggs (HR, 0.93 [95% CI, 0.88–0.99] per 20-g/d increment); the associations with yogurt and eggs were attenuated and nonsignificant after exclusion of the first 4 years of follow-up. Risk was not significantly associated with intakes of poultry, fish, or milk. In analyses modeling dietary substitutions, replacement of 100 kcal/d from red and processed meat with 100 kcal/d from fatty fish, yogurt, cheese, or eggs was associated with ≈20% lower risk of IHD. Consumption of red and processed meat was positively associated with serum non–high-density lipoprotein cholesterol concentration and systolic blood pressure, and consumption of cheese was inversely associated with serum non–high-density lipoprotein cholesterol. Conclusions: Risk for IHD was positively associated with consumption of red and processed meat and inversely associated with consumption of yogurt, cheese, and eggs, although the associations with yogurt and eggs may be influenced by reverse causation bias. It is not clear whether the associations with red and processed meat and cheese reflect causality, but they were consistent with the associations of these foods with plasma non–high-density lipoprotein cholesterol and for red and processed meat with systolic blood pressure, which could mediate such effects. Supplemental Digital Content is available in the text.

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    Apollo
    Other literature type . 2019
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    Apollo
    Article . 2019
    Data sources: Datacite
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    NARCIS
    Article . 2019
    Data sources: NARCIS
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      Apollo
      Other literature type . 2019
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      Apollo
      Article . 2019
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      Article . 2019
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    Authors: Ruiz-Arenas, Carlos; Bustamante, Mariona;

    To test associations between DNA methylation levels and gene expression levels in cis (cis eQTMs), we paired each Gene to CpGs closer than 500 kb from its TSS, either upstream or downstream. For each Gene, the TSS was defined based on HTA-2.0 annotation, using the start position for transcripts in the + strand, and the end position for transcripts in the - strand. CpGs position was obtained from Illumina 450K array annotation. Only CpGs in autosomal chromosomes (from chromosome 1 to 22) were tested. In the main analysis, we fitted for each CpG-Gene pair a linear regression model between gene expression and methylation levels adjusted for age, sex, cohort, and blood cell type composition. A second model was run without adjusting for blood cellular composition and it is only reported on the online web catalog, but not discussed in this manuscript. Although some of the unique associations of the unadjusted model might be real, others might be confounded by the large methylation and expression changes among blood cell types. To ensure that CpGs paired to a higher number of Genes do not have higher chances of being part of an eQTM, multiple-testing was controlled at the CpG level, following a procedure previously applied in the Genotype-Tissue Expression (GTEx) project (Gamazon et al., 2018). Briefly, our statistic used to test the hypothesis that a pair CpG-Gene is significantly associated is based on considering the lowest p-value observed for a given CpG and all its paired Gene (e.g., those in the 1 Mb window centered at the TSS). As we do not know the distribution of this statistic under the null, we used a permutation test. We generated 100 permuted gene expression datasets and ran our previous linear regression models obtaining 100 permuted p-values for each CpG-Gene pair. Then, for each CpG, we selected among all CpG-Gene pairs the minimum p-value in each permutation and fitted a beta distribution that is the distribution we obtain when dealing with extreme values (e.g. minimum) (Dudbridge and Gusnanto, 2008). Next, for each CpG, we took the minimum p-value observed in the real data and used the beta distribution to compute the probability of observing a lower p-value. We defined this probability as the empirical p-value of the CpG. Then, we considered as significant those CpGs with empirical p-values to be significant at 5% false discovery rate using Benjamini-Hochberg method. Finally, we applied a last step to identify all significant CpG-Gene pairs for all eCpGs. To do so, we defined a genome-wide empirical p-value threshold as the empirical p-value of the eCpG closest to the 5% false discovery rate threshold. We used this empirical p-value to calculate a nominal p-value threshold for each eCpG, based on the beta distribution obtained from the minimum permuted p-values. This nominal p-value threshold was defined as the value for which the inverse cumulative distribution of the beta distribution was equal to the empirical p-value. Then, for each eCpG, we considered as significant all eCpG-Gene variants with a p-value smaller than nominal p-value. For the meQTLs catalogue, we selected 9.9 M cis and trans meQTLs with a p-value <1e-7 in the ARIES dataset consisting of data from children of 7 years old (Gaunt et al., 2016). Then, we tested whether this subset of 9.9 M SNPs were also meQTLs in HELIX by running meQTL analyses using MatrixEQTL R package (Shabalin, 2012), adjusting for cohort, sex, age, blood cellular composition and the first 20 principal components (PCs) calculated from genome-wide genetic data of the GWAS variability. We confirmed 2.8 M meQTLs in HELIX (p-value <1e-7). Trans meQTLs represented <10% of the 2.8 M meQTLs. Enrichment of eCpGs for meQTLs was computed using a Chi-square test, using non eCpGs as background. Finally, we tested whether meQTLs were also eQTLs for the eGenes linked to the eCpGs. To this end, we run eQTL analyses (gene expression being the outcome and 2.8 M SNPs the predictors) with MatrixEQTL adjusting for cohort, sex, age, blood cellular composition and the first 20 GWAS PCs in HELIX. We considered as significant eQTLs the SNP-Gene pairs with p-value <1e-7 and with the direction of the effect consistent with the direction of the meQTL and the eQTM. Background: The identification of expression quantitative trait methylation (eQTMs), defined as associations between DNA methylation levels and gene expression, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis eQTMs in children’s blood, using data from 832 children of the Human Early Life Exposome (HELIX) project. Methods: Blood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (1 Mb window centered at the transcription start site, TSS) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, cohort, and blood cell composition. Results: We identified 39,749 blood autosomal cis eQTMs, representing 21,966 unique CpGs (eCpGs, 5.7% of total CpGs) and 8,886 unique transcript clusters (eGenes, 15.3% of total transcript clusters, equivalent to genes). In 87.9% of these cis eQTMs, the eCpG was located at <250 kb from eGene’s TSS; and 58.8% of all eQTMs showed an inverse relationship between the methylation and expression levels. Only around half of the autosomal cis-eQTMs eGenes could be captured through annotation of the eCpG to the closest gene. eCpGs had less measurement error and were enriched for active blood regulatory regions and for CpGs reported to be associated with environmental exposures or phenotypic traits. 40.4% of eQTMs had at least one genetic variant associated with methylation and expression levels. The overlap of autosomal cis eQTMs in children’s blood with those described in adults was small (13.8%), and age-shared cis eQTMs tended to be proximal to the TSS and enriched for genetic variants. See HELIX_Blood_eQTM_READMEfile_20210205.xlsx.

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    ZENODO
    Dataset . 2021
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    DRYAD
    Dataset . 2021
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      ZENODO
      Dataset . 2021
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      Dataset . 2021
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    Authors: Puljung, Michael; Vedovato, Natascia; Usher, Samuel; Ashcroft, Frances;

    The response of ATP-sensitive K+ channels (KATP) to cellular metabolism is coordinated by three classes of nucleotide binding site (NBS). We used a novel approach involving labeling of intact channels in a native, membrane environment with a non-canonical fluorescent amino acid and measurement (using FRET with fluorescent nucleotides) of steady-state and time-resolved nucleotide binding to dissect the role of NBS2 of the accessory SUR1 subunit of KATP in channel gating. Binding to NBS2 was Mg2+-independent, but Mg was required to trigger a conformational change in SUR1. Mutation of a lysine (K1384A) in NBS2 that coordinates bound nucleotides increased the EC50 for trinitrophenyl-ADP binding to NBS2, but only in the presence of Mg2+, indicating that this mutation disrupts the ligand-induced conformational change. Comparison of nucleotide-binding with ionic currents suggests a model in which each nucleotide binding event to NBS2 of SUR1 is independent and promotes KATP activation by the same amount. Puljung_data_sets

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    ZENODO
    Dataset . 2019
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    DANS-EASY
    Dataset . 2019
    Data sources: B2FIND
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    DRYAD; NARCIS
    Dataset . 2019
    License: CC 0
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      ZENODO
      Dataset . 2019
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      DANS-EASY
      Dataset . 2019
      Data sources: B2FIND
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      DRYAD; NARCIS
      Dataset . 2019
      License: CC 0
      Data sources: Datacite; NARCIS
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    Authors: Richter, Sandy; Helm, Conrad; Meunier, Frederic A.; Hering, Lars; +6 Authors

    This work was supported by Cancer Research UK [GS], a Wellcome Trust Senior Investigator Award (107116/Z/15/Z) [GS], and University College London [GS]. This work was further supported by the German Research Foundation (DFG; grant BL787/7-1) and an EU ASSEMBLE grant (No. 227799; http://www.assemblemarine.org) to CB. We acknowledge support from the German Research Foundation (DFG) and Universität Leipzig within the program of Open Access Publishing. [Background]: We present the first molecular characterization of glycerotoxin (GLTx), a potent neurotoxin found in the venom of the bloodworm Glycera tridactyla (Glyceridae, Annelida). Within the animal kingdom, GLTx shows a unique mode of action as it can specifically up-regulate the activity of Cav2.2 channels (N-type) in a reversible manner. The lack of sequence information has so far hampered a detailed understanding of its mode of action. [Conclusions]: Our results overturn a century old textbook view on the glycerid venom system, suggesting that it is anatomically and functionally much more complex than previously thought. The herein presented GLTx sequence information constitutes an important step towards the establishment of GLTx as a versatile tool to understand the mechanism of synaptic function, as well as the mode of action of this novel neurotoxin. [Results]: Our analyses reveal three ~3.8 kb GLTx full-length transcripts, show that GLTx represents a multigene family, and suggest it functions as a dimer. An integrative approach using transcriptomics, quantitative real-time PCR, in situ hybridization, and immunocytochemistry shows that GLTx is highly expressed exclusively in four pharyngeal lobes, a previously unrecognized part of the venom apparatus. © The Author(s). Peer Reviewed

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Leathlobhair, Máire Ní; Perri, Angela R.; Irving-Pease, Evan K.; Witt, Kelsey E.; +46 Authors

    Dogs were present in the Americas prior to the arrival of European colonists, but the origin and fate of these pre-contact dogs are largely unknown. We sequenced 71 mitochondrial and seven nuclear genomes from ancient North American and Siberian dogs spanning ~9,000 years. Our analysis indicates that American dogs were not domesticated from North American wolves. Instead, American dogs form a monophyletic lineage that likely originated in Siberia and dispersed into the Americas alongside people. After the arrival of Europeans, native American dogs almost completely disappeared, leaving a minimal genetic legacy in modern dog populations. Remarkably, the closest detectable extant lineage to pre-contact American dogs is the canine transmissible venereal tumor, a contagious cancer clone derived from an individual dog that lived up to 8,000 years ago. Mitochondrial DNA FASTA fileFASTA file containing 1166 dog mtDNA genomes used in this studyfull_mtDNA_alignment.fastaNEXUS treeMaximum likelihood tree (RAxML) of 1166 dogs mtDNA genomes used in this studyfull_mtDNA_alignment.treExcel sheetPublication source of the 1166 mtDNA genomes used in this studyfull_mtDNA_alignment.xlsxPlink (bed) fileContains genotype for dogs 54 dogsfull_data.bedPlink file (bim)Contains genotype for 54 dogsfull_data.bimPlink file (fam)Contains genotype for 54 dogsfull_data.famNJ tree in Figure 2bNJ tree in Figure 2b (see Table S2 for more info)Figure_b.treNexus fileNexus file used for producing Figure S12 (MKV model in MrBayes)Binary_char_MKV.nexNEXUS treeBayesian tree in Figure S12 (see Table S2 for more info)Figure_S12.tre

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    DRYAD; NARCIS
    Dataset . 2019
    License: CC 0
    Data sources: Datacite; NARCIS
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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
    Borealis
    Dataset . 2021
    Data sources: Datacite
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      Dataset . 2019
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      Dataset . 2018
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      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
      Borealis
      Dataset . 2021
      Data sources: Datacite
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