{"_links":{"self":{"href":"/api/v2/datasets/"},"first":{"href":"/api/v2/datasets/"},"last":{"href":"/api/v2/datasets/?page=3502"},"next":{"href":"/api/v2/datasets/?page=2"}},"count":20,"total":70023,"_embedded":{"stash:datasets":[{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.7rh4625"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.7rh4625/versions"},"stash:version":{"href":"/api/v2/versions/124910"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.7rh4625/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.7rh4625","id":93,"storageSize":999931789,"relatedPublicationISSN":"0960-9822","title":"Distinct predatory behaviors in scimitar- and dirk-toothed sabertooth cats","authors":[{"firstName":"Borja","lastName":"Figueirido","email":"borja.figueirido@uma.es","affiliation":"Universidad de Málaga","affiliationROR":"https://ror.org/036b2ww28","affiliations":[{"name":"Universidad de Málaga","ror_id":"https://ror.org/036b2ww28"}],"orcid":"0000-0003-2542-3977"},{"firstName":"Stephan","lastName":"Lautenschlager","affiliation":"University of Birmingham","affiliationROR":"https://ror.org/03angcq70","affiliations":[{"name":"University of Birmingham","ror_id":"https://ror.org/03angcq70"}]},{"firstName":"Alejandro","lastName":"Perez-Ramos","email":"","affiliation":"Universidad de Málaga","affiliationROR":"https://ror.org/036b2ww28","affiliations":[{"name":"Universidad de Málaga","ror_id":"https://ror.org/036b2ww28"}]},{"firstName":"Blaire","lastName":"Van Valkenburgh","affiliation":"University of California System","affiliationROR":"https://ror.org/00pjdza24","affiliations":[{"name":"University of California System","ror_id":"https://ror.org/00pjdza24"}]}],"abstract":"\u003cp\u003eOver the Cenozoic, large cat-like forms have convergently evolved into specialized killers of ‘megaherbivores’ that relied on their large, and laterally-compressed (saber-like) canines to rapidly subdue their prey [1-5]. Scimitar- and dirk-toothed sabertooths are distinct ecomorphs that differ in canine tooth length, degree of serration, and postcranial features indicative of dissimilar predatory behavior [6-13]. Despite these differences, it is assumed that they used a similar ‘canine-shear’ bite to kill their prey [14,15]. We investigated the killing behavior of the scimitar-toothed Homotherium serum and the dirk-toothed Smilodon fatalis using a comparative sample of living carnivores and a new quantitative approach to the analysis of skull function. For the first time, we quantified differences in the relative amount and distribution of cortical and trabecular bone in coronal sections of skulls to assess relative skull stiffness and flexibility [16-19]. We also use finite element analysis to simulate various killing scenarios that load skulls in ways that likely favor distinct proportions of cortical versus trabecular bone across the skull. Our data reveal that S. fatalis had an extremely thick skull and relatively little trabecular bone, consistent with a large investment in cranial strength for a stabbing canine-shear-bite. However, H. serum had more trabecular bone, and likely deployed an unusual predatory behavior more similar to the clamp-and-hold technique of the lion than S. fatalis. These data broaden the killing repertoire of sabertooths and highlight the degree of ecological specialization among members of the large carnivore guild during the Late Pleistocene of North America.\u003c/p\u003e\r\n","funders":[{"organization":"Spanish Ministry of Science, Innovation, and Universities","identifier":"","awardNumber":"BES-2013-065469"},{"organization":"Spanish Ministry of Science, Innovation, and Universities","identifier":"","awardNumber":"CGL2012"},{"organization":"Spanish Ministry of Science, Innovation, and Universities*","identifier":"","awardNumber":"37866"},{"organization":"Spanish Ministry of Science, Innovation, and Universities","identifier":"","awardNumber":"68300P"},{"organization":"Spanish Ministry of Science, Innovation, and Universities*","identifier":"","awardNumber":"CGL2015"}],"keywords":["cranial biomechanics","Paleobiology","trabecular bone","Finite element analysis","Crocuta crocuta","killing bite","scimitar-tooths","Lycaon pictus","Smilodon fatalis","Homotherium serum","dirk-tooths","Holocene","cortical bone"],"locations":[{"place":"Africa"},{"place":"North America"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.cub.2018.08.012"}],"versionNumber":2,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2021-06-15","lastModificationDate":"2023-12-14","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.7rh4625","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":912,"downloads":174,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.r8d4q"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.r8d4q/versions"},"stash:version":{"href":"/api/v2/versions/94"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.r8d4q/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.r8d4q","id":94,"storageSize":997532058,"relatedPublicationISSN":"0960-9822","title":"Data from: Phylogenomic insights into the evolution of stinging wasps and the origins of ants and bees","authors":[{"firstName":"Michael G.","lastName":"Branstetter","affiliation":"University of Utah","affiliationROR":"https://ror.org/03r0ha626","affiliations":[{"name":"University of Utah","ror_id":"https://ror.org/03r0ha626"}]},{"firstName":"Bryan N.","lastName":"Danforth","affiliation":"Utah State University","affiliationROR":"https://ror.org/00h6set76","affiliations":[{"name":"Utah State University","ror_id":"https://ror.org/00h6set76"}]},{"firstName":"James P.","lastName":"Pitts","affiliation":"Utah State University","affiliationROR":"https://ror.org/00h6set76","affiliations":[{"name":"Utah State University","ror_id":"https://ror.org/00h6set76"}]},{"firstName":"Brant C.","lastName":"Faircloth","affiliation":"Louisiana State University of Alexandria","affiliationROR":"https://ror.org/043z25g17","affiliations":[{"name":"Louisiana State University of Alexandria","ror_id":"https://ror.org/043z25g17"}]},{"firstName":"Philip S.","lastName":"Ward","affiliation":"University of California System","affiliationROR":"https://ror.org/00pjdza24","affiliations":[{"name":"University of California System","ror_id":"https://ror.org/00pjdza24"}]},{"firstName":"Matthew L.","lastName":"Buffington","affiliation":"Smithsonian Institution","affiliationROR":"https://ror.org/01pp8nd67","affiliations":[{"name":"Smithsonian Institution","ror_id":"https://ror.org/01pp8nd67"}]},{"firstName":"Michael W.","lastName":"Gates","affiliation":"Smithsonian Institution","affiliationROR":"https://ror.org/01pp8nd67","affiliations":[{"name":"Smithsonian Institution","ror_id":"https://ror.org/01pp8nd67"}]},{"firstName":"Robert R.","lastName":"Kula","affiliation":"Smithsonian Institution","affiliationROR":"https://ror.org/01pp8nd67","affiliations":[{"name":"Smithsonian Institution","ror_id":"https://ror.org/01pp8nd67"}]},{"firstName":"Seán G.","lastName":"Brady","affiliation":"Smithsonian Institution","affiliationROR":"https://ror.org/01pp8nd67","affiliations":[{"name":"Smithsonian Institution","ror_id":"https://ror.org/01pp8nd67"}]}],"abstract":"The stinging wasps (Hymenoptera: Aculeata) are an extremely diverse lineage of hymenopteran insects, encompassing over 70,000 described species and a diversity of life history traits, including ectoparasitism, cleptoparasitism, predation, pollen feeding (bees [Anthophila] and Masarinae) and eusociality (social vespid wasps, ants, and some bees) [1]. The most well-studied lineages of Aculeata are the ants, which are ecologically dominant in most terrestrial ecosystems [2], and the bees, the most important lineage of angiosperm-pollinating insects [3]. Establishing the phylogenetic affinities of ants and bees helps us understand and reconstruct patterns of social evolution as well as fully appreciate the biological implications of the switch from carnivory to pollen feeding (pollenivory). Despite recent advancements in aculeate phylogeny [4–11], considerable uncertainty remains regarding higher level relationships within Aculeata, including the phylogenetic affinities of ants and bees [5–7]. We used ultraconserved element (UCE) phylogenomics [7,12] to resolve relationships among stinging wasp families, gathering sequence data from \u003e 800 UCE loci and 187 samples, including 30 out of 31 aculeate families. We analyzed the 187-taxon data set using multiple analytical approaches, and we evaluated several alternative taxon sets. We also tested alternative hypotheses for the phylogenetic positions of ants and bees. Our results present a highly supported phylogeny of the stinging wasps. Most importantly, we find unequivocal evidence that ants are the sister group to bees+apoid wasps (Apoidea) and that bees are nested within a paraphyletic Crabronidae. We also demonstrate that taxon choice can fundamentally impact tree topology and clade support in phylogenomic inference.","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"DEB-1354996, DEB-0814544, DEB-0742998, DEB-1555905"}],"keywords":["Next-generation sequencing","social insects","Anthophila","taxon sampling","Molecular systematics","Aculeata","Apocrita"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eRaw sequence reads.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRaw Illumina reads for all newly sequenced samples from this study.\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eAlignment supermatrices.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll alignment supermatrices from this study. In NEXUS format. Includes UCE locus character set information.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eall-alignment-supermatrices.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eBEAST XML data files.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll BEAST XML data files generated for this study.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eall-beast-xml-files.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ePhylogenetic trees.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll phylogenetic trees generated in this study.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eall-phylogenetic-trees-results.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eData tables.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll data tables from this study.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eall-tables.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ePartitioning files.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ePartitioning files for the Hym-187T-F75 alignment supermatrix. Includes bylocus, hcluster, rcluster, and kmeans partitioning schemes. In RAxML format.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003epartition-files-for-hym-187t-f75-supermatrix.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eAlternative topologies for SH-tests.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll alternative topologies used to test the position of ants and bees. SH-tests were performed using RAxML.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esh-test-alternative-topologies.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSupplemental figures S1 and S2 combined.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThe supplemental figures combined into a single pdf figure.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esupplemental-figure-complete.pdf\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eUCE contigs - aligned, untrimmed, and unfiltered.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eTrinity contigs containing UCE loci. The contigs have been extracted from the bulk set of Trinity contigs using Phyluce and aligned, but they have not been trimmed to remove ambiguously aligned sites or filtered to remove loci with missing data.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003euce-aligments-untrimmed-unfiltered.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eUCE loci from genome-enabled samples.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eUCE loci extracted from available genome-enabled Hymenoptera. Each UCE locus consists of a core region plus 400 bp of flanking DNA on either side. Includes a lastz match-counts database and fasta files for use with Phyluce.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003euce-loci-extracted-from-genome-enabled-samples.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eTrinity assemblies - bulk.\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThe bulk set of Trinity assemblies for all enriched samples analyzed in this study. Includes assemblies from the previous Faircloth et al. (2015) study. Cleaned reads were assembled using Trinity ver. r2013-02-25.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003etrinity-assemblies-all-samples.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eBaCoCa results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eBaCoCa analysis results. Performed on the Hym-187T-F75 locus set.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003ebacoca-results.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"global"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.cub.2017.03.027"},{"relationship":"dataset","identifierType":"URL","identifier":"https://www.ncbi.nlm.nih.gov/nuccore/..%2Fbioproject%2F%3Fterm%3DPRJNA379583"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2018-04-03","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.r8d4q","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":1112,"downloads":178,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2d7b8"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2d7b8/versions"},"stash:version":{"href":"/api/v2/versions/95"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2d7b8/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.2d7b8","id":95,"storageSize":997366561,"relatedPublicationISSN":"0962-1083","title":"Data from: Complex selection on a regulator of social cognition: evidence of balancing selection, regulatory interactions and population differentiation in the prairie vole Avpr1a locus","authors":[{"firstName":"Alejandro","lastName":"Berrio","affiliation":"The University of Texas at Austin","affiliationROR":"https://ror.org/00hj54h04","affiliations":[{"name":"Duke University","ror_id":"https://ror.org/00py81415"},{"name":"The University of Texas at Austin","ror_id":"https://ror.org/00hj54h04"}]},{"firstName":"Rafael F.","lastName":"Guerrero","affiliation":"Indiana University Bloomington","affiliationROR":"https://ror.org/02k40bc56","affiliations":[{"name":"Indiana University Bloomington","ror_id":"https://ror.org/02k40bc56"}]},{"firstName":"Galina V.","lastName":"Aglyamova","affiliation":"The University of Texas at Austin","affiliationROR":"https://ror.org/00hj54h04","affiliations":[{"name":"The University of Texas at Austin","ror_id":"https://ror.org/00hj54h04"}]},{"firstName":"Mariam","lastName":"Okhovat","affiliation":"The University of Texas at Austin","affiliationROR":"https://ror.org/00hj54h04","affiliations":[{"name":"The University of Texas at Austin","ror_id":"https://ror.org/00hj54h04"}]},{"firstName":"Mikhail V.","lastName":"Matz","affiliation":"The University of Texas at Austin","affiliationROR":"https://ror.org/00hj54h04","affiliations":[{"name":"The University of Texas at Austin","ror_id":"https://ror.org/00hj54h04"}]},{"firstName":"Steven M.","lastName":"Phelps","affiliation":"The University of Texas at Austin","affiliationROR":"https://ror.org/00hj54h04","affiliations":[{"name":"The University of Texas at Austin","ror_id":"https://ror.org/00hj54h04"}]}],"abstract":"Adaptive variation in social behavior depends upon standing genetic variation, but we know little about how evolutionary forces shape genetic diversity relevant to brain and behavior. In prairie voles (Microtus ochrogaster), variants at the Avpr1a locus predict expression of the vasopressin 1a receptor in the retrosplenial cortex (RSC), a brain region that mediates spatial and contextual memory; cortical V1aR abundance in turn predicts diversity in space-use and sexual fidelity in the field. To examine the potential contributions of adaptive and neutral forces to variation at the Avpr1a locus, we explore sequence diversity at the Avpr1a locus and throughout the genome in two populations of wild prairie voles. First, we refine results demonstrating balancing selection at the locus by comparing the frequency spectrum of variants at the locus to a random sample of the genome. Next, we find that the four SNPs that predict high V1aR expression in the RSC are in stronger linkage disequilibrium than expected by chance despite high recombination among intervening variants, suggesting that epistatic selection maintains their association despite recombination. Analysis of population structure and a haplotype network for two populations revealed that this excessive LD was unlikely to be due to admixture alone. Furthermore, the two populations differed considerably in the region shown to be a regulator of V1aR expression despite the extremely low levels of genome-wide genetic differentiation. Together, our data suggest that complex selection on Avpr1a locus favors specific combinations of regulatory polymorphisms, maintains the resulting alleles at populations-specific frequencies, and may contribute to unique patterns of spatial cognition and sexual fidelity among populations.","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"IOS-1457350, IOS-1355188"}],"keywords":["Gene regulation","Sexual Fidelity","Microtus ochrogaster","Behavior/Social Evolution","Holocene"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eData and scripts to plot and analyse complex selection in two populations of prairie voles\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThis repository contains the data necessary to plot each figure from 1 to 4, and S1 and S2. Also in folder Haploview_Slider_PopArt_data, we include the nucleotide alignments and and output data from haploview, and input data for popart, slider and dnasp5. In the root, we included two excel files. Raw scores of haplotypes and data procesing for generating input files for haploview and phase.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eBerrio2017.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"Jackson County"},{"place":"IL"},{"place":"Champaign County"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/mec.14455"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2017-11-07","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.2d7b8","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":401,"downloads":59,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.s3j9074"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.s3j9074/versions"},"stash:version":{"href":"/api/v2/versions/96"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.s3j9074/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.s3j9074","id":96,"storageSize":996067863,"relatedPublicationISSN":"0003-0147","title":"Data from: The evolution of marine larval dispersal kernels in spatially structured habitats: analytical models, individual-based simulations, and comparisons with empirical estimates","authors":[{"firstName":"Allison K.","lastName":"Shaw","affiliation":"University of Minnesota","affiliationROR":"https://ror.org/017zqws13","affiliations":[{"name":"University of Minnesota","ror_id":"https://ror.org/017zqws13"}]},{"firstName":"Cassidy C.","lastName":"D'Aloia","affiliation":"Woods Hole Oceanographic Institution","affiliationROR":"https://ror.org/03zbnzt98","affiliations":[{"name":"Woods Hole Oceanographic Institution","ror_id":"https://ror.org/03zbnzt98"}]},{"firstName":"Peter M.","lastName":"Buston","affiliation":"Boston University","affiliationROR":"https://ror.org/05qwgg493","affiliations":[{"name":"Boston University","ror_id":"https://ror.org/05qwgg493"}]}],"abstract":"Understanding the causes of larval dispersal is a major goal of marine ecology, yet most research focuses on proximate causes. Here, we ask how ultimate, evolutionary causes affect dispersal. Building on Hamilton and May's 1977 classic paper (``Dispersal in stable habitats\"), we develop analytic and simulation models for the evolution of dispersal kernels in spatially structured habitats. First, we investigate dispersal in a world without edges and find that most offspring disperse as far as possible, opposite the pattern of empirical data. Adding edges to our model world leads to nearly all offspring dispersing short distances, again a mismatch with empirical data. Adding resource heterogeneity improves our results: most offspring disperse short distances with some dispersing longer distances. Finally, we simulate dispersal evolution in a real seascape in Belize and find that the simulated dispersal kernel and an empirical dispersal kernel from that seascape both have the same shape, with a high level of short-distance dispersal and a low level of long-distance dispersal. The novel contribution of this work is to provide a spatially explicit analytic extension of Hamilton and May 1977, to demonstrate that our spatially explicit simulations and analytic models provide equivalent results, and to use simulation approaches to investigate the evolution of dispersal kernel shape in spatially complex habitats. Our model could be modified in various ways to investigate dispersal evolution in other species and seascapes, providing new insights into patterns of marine larval dispersal.","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"OCE-1260424"}],"keywords":["Ecology: evolutionary"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eREADME\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eContains readme and notes for all files.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eREADME.txt\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efiles_runsims\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eContains Matlab code (.m files) for running model simulation data used for Figures 2-4 in the paper.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eoutput_environments\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eContains Matlab data files (.mat files) for each type of simulation environment.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eoutput_simulations\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eContains Matlab data files (.mat files) for each individual-based simulation.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efiles_plotsims\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eContains Matlab code (.m files) for plotting Figures 2-4 in the paper, using the above .mat files.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efiles_plotcomparisons\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eContains Matlab code (.m files), R code (.R files) and data files (.csv) to compare simulated and empirical data and to plot Figure 5 in the paper.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1086/701667"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2018-11-07","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.s3j9074","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":345,"downloads":20,"citations":2}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.08vv50n"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.08vv50n/versions"},"stash:version":{"href":"/api/v2/versions/97"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.08vv50n/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.08vv50n","id":97,"storageSize":994990868,"relatedPublicationISSN":"1439-4227","title":"Data from: Gene discovery in Gelsemium highlights conserved gene clusters in monoterpene indole alkaloid biosynthesis","authors":[{"firstName":"Jakob","lastName":"Franke","affiliation":"John Innes Centre","affiliationROR":"https://ror.org/055zmrh94","affiliations":[{"name":"John Innes Centre","ror_id":"https://ror.org/055zmrh94"}]},{"firstName":"Jeongwoon","lastName":"Kim","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"John P.","lastName":"Hamilton","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Dongyan","lastName":"Zhao","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Gina M.","lastName":"Pham","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Krystle","lastName":"Wiegert-Rininger","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Emily","lastName":"Crisovan","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Linsey","lastName":"Newton","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Brieanne","lastName":"Vaillancourt","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Evangelos","lastName":"Tatsis","affiliation":"John Innes Centre","affiliationROR":"https://ror.org/055zmrh94","affiliations":[{"name":"John Innes Centre","ror_id":"https://ror.org/055zmrh94"}]},{"firstName":"C. Robin","lastName":"Buell","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Sarah E.","lastName":"O'Connor","affiliation":"John Innes Centre","affiliationROR":"https://ror.org/055zmrh94","affiliations":[{"name":"John Innes Centre","ror_id":"https://ror.org/055zmrh94"}]}],"abstract":"[No abstract filled]","keywords":["Alkaloid","Gelsimium","Biosynthesis"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003egel_v1_asm.fasta\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGenome assembly of Gelsemium sempervirens (version 1), containing 3,352 scaffold sequences\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ecro_v2_asm.fasta\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGenome assembly of Catharanthus roseus (version 2), containing 2,090 scaffold sequences\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003egel_v1.gene_models.gff3\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGene Model Annotation for Gelsemium sempervirens (version 1) in Generic Feature Format 3 (GFF3)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003egel_v1.transcripts.fasta\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGelsemium sempervirens (version 1) transcript sequences\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003egel_v1.proteins.fasta\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGelsemium sempervirens (version 1) protein sequences\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ecro_v2.gene_models.gff3\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGene Model Annotation for Catharanthus roseus (version 2) in Generic Feature Format 3 (GFF3)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ecro_v2.transcripts.fasta\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eCatharanthus roseus (version 2) transcript sequences\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ecro_v2.proteins.fasta\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eCatharanthus roseus (version 2) protein sequences\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eREADME.txt\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eREADME.txt\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eREADME.txt\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1002/cbic.201800592"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2018-10-25","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.08vv50n","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":3812,"downloads":1024,"citations":2}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.n2q7f"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.n2q7f/versions"},"stash:version":{"href":"/api/v2/versions/35333"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.n2q7f/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.n2q7f","id":98,"storageSize":994682183,"relatedPublicationISSN":"1755-0998","title":"Data from: \"Comparative genomic resources for spiny lizards (genus Sceloporus)\" in Genomic Resources Notes accepted 1 August 2014-30 September 2014","authors":[{"firstName":"Rebecca B.","lastName":"Harris","email":"rharri35@asu.edu","affiliation":"Arizona State University","affiliationROR":"https://ror.org/03efmqc40","affiliations":[{"name":"Arizona State University","ror_id":"https://ror.org/03efmqc40"}]},{"firstName":"Adam D.","lastName":"Leaché","email":"","affiliation":"University of Washington","affiliationROR":"https://ror.org/00cvxb145","affiliations":[{"name":"University of Washington","ror_id":"https://ror.org/00cvxb145"}]}],"abstract":"To advance comparative genomic studies of the spiny fence lizards (genus Sceloprous), we provide the genomic annotations for 35 Sceloporus species.","keywords":["Reptiles","Sceloporus clarkii","Sceloporus carinatus","Comparative Biology","Sceloporus malachiticus","Sceloporus zosteromus","Sceloporus occidentalis","Sceloporus licki","Sceloporus olivaceus","Sceloporus exsul","Gene Structure and Function","Sceloporus ochoterenae","Sceloporus tristichus","Sceloporus edwardtaylori","Sceloporus hunsakeri","Sceloporus jalapae","Sceloporus spinosus","Sceloporus angustus","Sceloporus bicanthalis","Sceloporus adleri","Sceloporus gadoviae","Sceloporus taeniocnemis","Sceloporus torquatus","Sceloporus scalaris","Sceloporus siniferus","Sceloporus smithi","Sceloporus formosus","Sceloporus grammicus","Sceloporus horridus","Sceloporus mucronatus","Sceloporus palaciosi","Sceloporus cowlesi","Sceloporus magister","Genomics/Proteomics","Sceloporus graciosus","Sceloporus woodi","Sceloporus variabilis","Sceloporus utiformis","Sceloporus orcutti"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSceloporus GFF3 annotation files\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAnnotation, gene ontology, and genomic sequences in GFF3 format\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esceloporus_gff.tar.gz\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eOrthologous groups\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eOrthologous groups predicted by OrthoMCL. Datasets included: 35 Sceloporus species, plus the Anolis carolinensis, Gallus gallus, and human proteins.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esceloporus_ortho_groups.txt\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/1755-0998.12340"}],"versionNumber":2,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2019-10-02","lastModificationDate":"2019-10-02","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.n2q7f","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":640,"downloads":91,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.4vg17"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.4vg17/versions"},"stash:version":{"href":"/api/v2/versions/35335"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.4vg17/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.4vg17","id":99,"storageSize":994533047,"relatedPublicationISSN":"1365-2699","title":"Data from: Demographic modelling reveals a history of divergence with gene flow for a glacially tied stonefly in a changing post-Pleistocene landscape","authors":[{"firstName":"Scott","lastName":"Hotaling","email":"scott.hotaling@wsu.edu","affiliation":"University of Kentucky","affiliationROR":"https://ror.org/02k3smh20","affiliations":[{"name":"University of Kentucky","ror_id":"https://ror.org/02k3smh20"}]},{"firstName":"Clint C.","lastName":"Muhlfeld","affiliation":"University of Montana","affiliationROR":"https://ror.org/0078xmk34","affiliations":[{"name":"University of Montana","ror_id":"https://ror.org/0078xmk34"}]},{"firstName":"J. Joseph","lastName":"Giersch","email":"","affiliation":"United States Geological Survey","affiliationROR":"https://ror.org/035a68863","affiliations":[{"name":"United States Geological Survey","ror_id":"https://ror.org/035a68863"}]},{"firstName":"Omar A.","lastName":"Ali","email":"","affiliation":"University of California, Davis","affiliationROR":"https://ror.org/05rrcem69","affiliations":[{"name":"University of California, Davis","ror_id":"https://ror.org/05rrcem69"}]},{"firstName":"Steve","lastName":"Jordan","affiliation":"Bucknell University","affiliationROR":"https://ror.org/00fc1qt65","affiliations":[{"name":"Bucknell University","ror_id":"https://ror.org/00fc1qt65"}]},{"firstName":"Michael R.","lastName":"Miller","email":"","affiliation":"University of California, Davis","affiliationROR":"https://ror.org/05rrcem69","affiliations":[{"name":"University of California, Davis","ror_id":"https://ror.org/05rrcem69"}]},{"firstName":"Gordon","lastName":"Luikart","affiliation":"University of Montana","affiliationROR":"https://ror.org/0078xmk34","affiliations":[{"name":"University of Montana","ror_id":"https://ror.org/0078xmk34"}]},{"firstName":"David W.","lastName":"Weisrock","affiliation":"University of Kentucky","affiliationROR":"https://ror.org/02k3smh20","affiliations":[{"name":"University of Kentucky","ror_id":"https://ror.org/02k3smh20"}]}],"abstract":"Aim: Climate warming is causing extensive loss of glaciers in mountainous regions, yet our understanding of how glacial recession influences evolutionary processes and genetic diversity is limited. Linking genetic structure with the influences shaping it can improve understanding of how species respond to environmental change. Here, we used genome-scale data and demographic modelling to resolve the evolutionary history of Lednia tumana, a rare, aquatic insect endemic to alpine streams. We also employed a range of widely used data filtering approaches to quantify how they influenced population structure results.\r\nLocation: Alpine streams in the Rocky Mountains of Glacier National Park, Montana, USA.\r\nTaxon: Lednia tumana, a stonefly (Order Plecoptera) in the family Nemouridae.\r\nMethods: We generated single nucleotide polymorphism data through restriction-site associated DNA sequencing to assess contemporary patterns of genetic structure for 11 L. tumana populations. Using identified clusters, we assessed demographic history through model selection and parameter estimation in a coalescent framework. During population structure analyses, we filtered our data to assess the influence of singletons, missing data and total number of markers on results.\r\nResults: Contemporary patterns of population structure indicate that L. tumana exhibits a pattern of isolation-by-distance among populations within three genetic clusters that align with geography. Mean pairwise genetic differentiation (FST) among populations was 0.033. Coalescent-based demographic modelling supported divergence with gene flow among genetic clusters since the end of the Pleistocene (~13-17 kya), likely reflecting the south-to-north recession of ice sheets that accumulated during the Wisconsin glaciation.\r\nMain conclusions: We identified a link between glacial retreat, evolutionary history and patterns of genetic diversity for a range-restricted stonefly imperiled by climate change. This finding included a history of divergence with gene flow, an unexpected conclusion for a mountaintop species. Beyond L. tumana, this study demonstrates the complexity of assessing genetic structure for weakly differentiated species, shows the degree to which rare alleles and missing data may influence results, and highlights the usefulness of genome-scale data to extend population genetic inquiry in non-model species.","keywords":["alpine stream ecology","Glacier National Park","Lednia tumana"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eDryad Accession for Hotaling et al. 2017, Journal of Biogeography\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRelevant files for the associated study: input files for PLINK and Fastsimcoal2, a key of individuals/populations, and raw SNP catalog as output by STACKS with a whitelist for the loci/SNPs used in this study.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eHotaling_2017_Dryad.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"Glacier National Park"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/jbi.13125"}],"versionNumber":2,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2018-10-12","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.4vg17","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":427,"downloads":45,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.77b2422"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.77b2422/versions"},"stash:version":{"href":"/api/v2/versions/72382"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.77b2422/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.77b2422","id":100,"storageSize":992698314,"relatedPublicationISSN":"2047-217X","title":"Data from: A chromosomal-scale genome assembly of Tectona grandis reveals the importance of tandem gene duplication and enables discovery of genes in natural product biosynthetic pathways","authors":[{"firstName":"Dongyan","lastName":"Zhao","email":"zhaodon4@msu.edu","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"John P.","lastName":"Hamilton","email":"jham@msu.edu","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}],"orcid":"0000-0002-8682-5526"},{"firstName":"Wajid Waheed","lastName":"Bhat","email":"bhatwaji@msu.edu","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}],"orcid":"0000-0002-7049-8671"},{"firstName":"Sean R.","lastName":"Johnson","email":"","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]},{"firstName":"Grant T.","lastName":"Godden","email":"g0ddengr@ufl.edu","affiliation":"University of Florida","affiliationROR":"https://ror.org/02y3ad647","affiliations":[{"name":"University of Florida","ror_id":"https://ror.org/02y3ad647"}],"orcid":"0000-0003-2628-4729"},{"firstName":"Taliesin J.","lastName":"Kinser","email":"tkinser@ufl.edu","affiliation":"University of Florida","affiliationROR":"https://ror.org/02y3ad647","affiliations":[{"name":"University of Florida","ror_id":"https://ror.org/02y3ad647"}],"orcid":"0000-0002-9497-5399"},{"firstName":"Benoît","lastName":"Boachon","email":"","affiliation":"Purdue University West Lafayette","affiliationROR":"https://ror.org/02dqehb95","affiliations":[{"name":"Purdue University West Lafayette","ror_id":"https://ror.org/02dqehb95"}]},{"firstName":"Natalia","lastName":"Dudareva","email":"dudareva@purdue.edu","affiliation":"Purdue University West Lafayette","affiliationROR":"https://ror.org/02dqehb95","affiliations":[{"name":"Purdue University West Lafayette","ror_id":"https://ror.org/02dqehb95"}],"orcid":"0000-0003-0777-7763"},{"firstName":"Douglas E.","lastName":"Soltis","email":"dsoltis@ufl.edu","affiliation":"University of Florida","affiliationROR":"https://ror.org/02y3ad647","affiliations":[{"name":"University of Florida","ror_id":"https://ror.org/02y3ad647"}]},{"firstName":"Pamela S.","lastName":"Soltis","email":"psoltis@flmnh.ufl.edu","affiliation":"University of Florida","affiliationROR":"https://ror.org/02y3ad647","affiliations":[{"name":"University of Florida","ror_id":"https://ror.org/02y3ad647"}],"orcid":"0000-0001-9310-8659"},{"firstName":"Bjoern","lastName":"Hamberger","email":"hamberge@msu.edu","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}],"orcid":"0000-0003-1249-1807"},{"firstName":"C. Robin","lastName":"Buell","email":"buell@msu.edu","affiliation":"Michigan State University","affiliationROR":"https://ror.org/05hs6h993","affiliations":[{"name":"Michigan State University","ror_id":"https://ror.org/05hs6h993"}]}],"abstract":"Background: Teak, a member of the Lamiaceae family, produces one of the most expensive hardwoods in the world. High demand coupled with deforestation have caused a decrease in natural teak forests, and future supplies will be reliant on teak plantations. Hence, selection of teak tree varieties for clonal propagation with superior growth performance is of great importance, and access to high-quality genetic and genomic resources can accelerate the selection process by identifying genes underlying desired traits.\r\nFindings: To facilitate teak research and variety improvement, we generated a highly contiguous, chromosomal-scale genome assembly using high-coverage PacBio long reads coupled with high-throughput chromatin conformation capture. Of the 18 teak chromosomes, we generated 17 near-complete pseudomolecules with one chromosome present as two chromosome arm scaffolds. Genome annotation yielded 31,168 genes encoding 46,826 gene models, of which, 39,930 and 41,155 had Pfam domain and expression evidence, respectively. We identified 14 clusters of tandem-duplicated terpene synthases (TPSs), genes central to the biosynthesis of terpenes which are involved in plant defense and pollinator attraction. Transcriptome analysis revealed 10 TPSs highly expressed in woody tissues, of which, 8 were in tandem, revealing the importance of resolving tandemly duplicated genes and the quality of the assembly and annotation. We also validated the enzymatic activity of four TPSs to demonstrate the function of key TPSs.\r\nConclusions: In summary, this high-quality chromosomal-scale assembly and functional annotation of the teak genome will facilitate the discovery of candidate genes related to traits critical for sustainable production of teak and for anti-insecticidal natural products.","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"IOS-1444499"}],"keywords":["chromosomal-scale assembly","tandem-duplicated genes","teak","terpene synthases"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_tectona_grandis_26Jun2018_7GlFM_fmt_tp.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003efasta sequences of the assembly\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak.working_models_HiC.cdna_con_sorted.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ecDNA sequences of all isoforms of the working gene set\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak.working_models_HiC.cds_con_sorted.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eCDS of all isoforms of the working gene set\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak.working_models_HiC.pep_con_sorted.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ePeptide sequence of all isoforms of the working gene set\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak.working_models_HiC_fmtDes_con_sorted.gff\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGFF of all isoforms of the working gene set\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_hc_models_HiC_con_sorted_modiGeneID.gff\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGFF of all high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_hc_models_HiC.cdna_con_sorted_modiGeneID.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ecDNA sequences of all high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_hc_models_HiC.cds_con_sorted_modiGeneID.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eCDS of all high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_hc_models_HiC.pep_con_sorted_modiGeneID.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ePeptide sequences of all high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_repr_hc_models_HiC_con_sorted_modiGeneID.gff\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eGFF of representative high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_repr_hc_models_HiC.cdna_con_sorted_modiGeneID.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ecDNA sequences of representative high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_repr_hc_models_HiC.cds_con_sorted_modiGeneID.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eCDS of representative high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_repr_hc_models_HiC.pep_con_sorted_modiGeneID.fa\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003ePeptide sequences of representative high-confidence gene models\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eteak_working_gene_fpkm_matrix_con_sorted.txt\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eExpression abundances of the working gene set were estimated using cufflinks RNAseq experiment atlas from NCBI SRA BioProject PRJNA287604\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003e2018.10.23-teak-data-readme.docx\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eReadme\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1093/gigascience/giz005"}],"versionNumber":7,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2020-07-15","lastModificationDate":"2020-07-15","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.77b2422","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":1641,"downloads":500,"citations":2}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9cp3j"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9cp3j/versions"},"stash:version":{"href":"/api/v2/versions/101"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.9cp3j/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.9cp3j","id":101,"storageSize":991316188,"relatedPublicationISSN":"2054-5703","title":"Data from: The chinchilla as a novel animal model of pregnancy","authors":[{"firstName":"Emmeli","lastName":"Mikkelsen","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Henrik","lastName":"Lauridsen","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Per Mose","lastName":"Nielsen","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Haiyun","lastName":"Qi","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Thomas","lastName":"Nørlinger","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Maria Dahl","lastName":"Andersen","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Niels","lastName":"Uldbjerg","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Christoffer","lastName":"Laustsen","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Puk","lastName":"Sandager","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]},{"firstName":"Michael","lastName":"Pedersen","affiliation":"Aarhus University Hospital","affiliationROR":"https://ror.org/040r8fr65","affiliations":[{"name":"Aarhus University Hospital","ror_id":"https://ror.org/040r8fr65"}]}],"abstract":"Several parameters are important when choosing the most appropriate animal to model human obstetrics, including gestation period, number of fetuses per gestation and placental structure. The domesticated long-tailed chinchilla (Chinchilla lanigera) is a well-suited and appropriate animal model of pregnancy that often will carry only one offspring and has a long gestation period of 105–115 days. Furthermore, the chinchilla placenta is of the haemomonochorial labyrinthine type and is therefore comparable to the human villous haemomonochorial placenta. This proof-of-concept study demonstrated the feasibility in laboratory settings, and demonstrated the potential of the pregnant chinchilla as an animal model for obstetric research and its potential usefulness for non-invasive measurements in the placenta. We demonstrate measurements of the placental and fetal metabolism (demonstrated in vivo by hyperpolarized MRI and in vitro by qPCR analyses), placental vessels (demonstrated ex vivo by contrast-enhanced CT angiography) and overall anatomy (demonstrated in vivo by whole-body CT).","keywords":["Pregnant animal models","Placenta","Imaging","Comparative Biology","Chinchilla"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eImaging data\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eData contains (zip format files): 1) reconstructed MRI data (General Electric MRI system) and regions-of-interests files. Excel spreadsheet is provided with signal intensity results. 2) CT whole-animal data (SIemens CT system). 3) CT angiography of placenta (Scanco CT system).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esupplementary data.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1098/rsos.161098"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2017-03-30","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.9cp3j","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":499,"downloads":31,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.r5nf0"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.r5nf0/versions"},"stash:version":{"href":"/api/v2/versions/102"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.r5nf0/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.r5nf0","id":102,"storageSize":990328081,"relatedPublicationISSN":"1558-5646","title":"Data from: Community trees: identifying codiversification in the páramo dipteran community","authors":[{"firstName":"Bryan Charles","lastName":"Carstens","affiliation":"The Ohio State University","affiliationROR":"https://ror.org/00rs6vg23","affiliations":[{"name":"The Ohio State University","ror_id":"https://ror.org/00rs6vg23"}]},{"firstName":"Michael","lastName":"Gruenstaeudl","affiliation":"Freie Universität Berlin","affiliationROR":"https://ror.org/046ak2485","affiliations":[{"name":"Freie Universität Berlin","ror_id":"https://ror.org/046ak2485"}]},{"firstName":"Noah M.","lastName":"Reid","affiliation":"University of California, Davis","affiliationROR":"https://ror.org/05rrcem69","affiliations":[{"name":"University of California, Davis","ror_id":"https://ror.org/05rrcem69"}]}],"abstract":"Groups of codistributed species that responded in a concerted manner to environmental events are expected to share patterns of evolutionary diversification. However, the identification of such groups has largely been based on qualitative, post hoc analyses. We develop here two methods (PPS, K-F ANOVA) for the analysis of codistributed species that, given a group of species with a shared pattern of diversification, allow empiricists to identify those taxa that do not codiversify (i.e., \"outlier\" species). The identification of outlier species makes it possible to jointly estimate the evolutionary history of co-diversifying taxa. To evaluate the approaches presented here, we collected data from Páramo dipterans, identified outlier species, and estimated a \"community tree\" from species that are identified as having co-diversified. Our results demonstrate that dipteran communities from different Páramo habitats in the same mountain range are more closely related than communities in other ranges. We also conduct simulation testing to evaluate this approach. Results suggest that our approach provides a useful addition to comparative phylogeographic methods, while identifying aspects of the analysis that require careful interpretation. In particular, both the PPS and K-F ANOVA perform acceptably when there are one or two outlier species, but less so as the number of outliers increase. This is likely a function of the corresponding degradation of the signal of community divergence; without a strong signal from a co-diversifying community, there is no dominant pattern from which to detect and outlier species. For this reason, both the magnitude of K-F distance distribution and outside knowledge about the phylogeographic history of each putative member of the community should be considered when interpreting results.","keywords":["Coalescent Theory","Dipteran communities","Pleistocene to recent","Páramo","Diptera","comparative phylogeography"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eCommunity_Trees\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003e1. Nexus files from Paramo fly species, containing aligned COI data described in text of manuscript. 2. Results from community tree analysis of Paramo fly COI data.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"Central America"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/evo.12916"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2016-03-23","lastModificationDate":"2020-06-30","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.r5nf0","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":337,"downloads":25,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.30mv0"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.30mv0/versions"},"stash:version":{"href":"/api/v2/versions/103"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.30mv0/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.30mv0","id":103,"storageSize":989481164,"relatedPublicationISSN":"0092-8674","title":"Data from: Aneuploidy causes non-genetic individuality","authors":[{"firstName":"Rebecca R.","lastName":"Beach","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}]},{"firstName":"Chiara","lastName":"Ricci-Tam","affiliation":"Harvard University","affiliationROR":"https://ror.org/03vek6s52","affiliations":[{"name":"Harvard University","ror_id":"https://ror.org/03vek6s52"}]},{"firstName":"Christopher M.","lastName":"Brennan","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}]},{"firstName":"Christine A.","lastName":"Moomau","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}]},{"firstName":"Pei-hsin","lastName":"Hsu","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}]},{"firstName":"Bo","lastName":"Hua","affiliation":"Harvard University","affiliationROR":"https://ror.org/03vek6s52","affiliations":[{"name":"Harvard University","ror_id":"https://ror.org/03vek6s52"}]},{"firstName":"Rebecca E.","lastName":"Silberman","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}]},{"firstName":"Michael","lastName":"Springer","affiliation":"Harvard University","affiliationROR":"https://ror.org/03vek6s52","affiliations":[{"name":"Harvard University","ror_id":"https://ror.org/03vek6s52"}]},{"firstName":"Angelika","lastName":"Amon","affiliation":"Massachusetts Institute of Technology","affiliationROR":"https://ror.org/042nb2s44","affiliations":[{"name":"Massachusetts Institute of Technology","ror_id":"https://ror.org/042nb2s44"}]}],"abstract":"Phenotypic variability is a hallmark of diseases involving chromosome gains and losses, such as Down syndrome and cancer. Allelic variances have been thought to be the sole cause of this heterogeneity. Here, we systematically examine the consequences of gaining and losing single or multiple chromosomes to show that the aneuploid state causes non-genetic phenotypic variability. Yeast cell populations harboring the same defined aneuploidy exhibit heterogeneity in cell-cycle progression and response to environmental perturbations. Variability increases with degree of aneuploidy and is partly due to gene copy number imbalances, suggesting that subtle changes in gene expression impact the robustness of biological networks and cause alternate behaviors when they occur across many genes. As inbred trisomic mice also exhibit variable phenotypes, we further propose that non-genetic individuality is a universal characteristic of the aneuploid state that may contribute to variability in presentation and treatment responses of diseases caused by aneuploidy.","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"1349248"}],"keywords":["Down syndrome","Mus musculus","Aneuploidy","gene dosage effects","cell-to-cell variability","biological noise","non-genetic heterogeneity"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFC Dataset: steady-state GAL pathway induction\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRaw FCS files and MATLAB analysis code. Flow cytometry analysis toolkit used in this code is available at https://github.com/springerlab\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eGAL induction steady-state.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFC Dataset: steady-state heat shock stress response\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRaw FCS files and MATLAB analysis code. Flow cytometry analysis toolkit used in this code is available at https://github.com/springerlab\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eHS stress steady-state.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFC Dataset: steady-state DTT stress response\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRaw FCS files and MATLAB analysis code. Flow cytometry analysis toolkit used in this code is available at https://github.com/springerlab\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eDTT stress steady-state.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFC Dataset: YFP/mCherry dual reporter noise assay\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRaw FCS files and MATLAB analysis code. Flow cytometry analysis toolkit used in this code is available at https://github.com/springerlab\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eDual-reporter assay.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFC Dataset: kinetic GAL pathway induction\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRaw FCS files and MATLAB analysis code. Flow cytometry analysis toolkit used in this code is available at https://github.com/springerlab\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eGAL induction kinetics.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1016/j.cell.2017.03.021"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2018-02-22","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.30mv0","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":501,"downloads":59,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.hf93m"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.hf93m/versions"},"stash:version":{"href":"/api/v2/versions/104"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.hf93m/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.hf93m","id":104,"storageSize":9994758981,"relatedPublicationISSN":"1932-6203","title":"Data from: Reporting tumor molecular heterogeneity in histopathological diagnosis","authors":[{"firstName":"Andrea","lastName":"Mafficini","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Eliana","lastName":"Amato","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Matteo","lastName":"Fassan","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Michele","lastName":"Simbolo","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Davide","lastName":"Antonello","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Caterina","lastName":"Vicentini","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Maria","lastName":"Scardoni","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Samantha","lastName":"Bersani","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Marisa","lastName":"Gottardi","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Borislav","lastName":"Rusev","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Giorgio","lastName":"Malpeli","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Vincenzo","lastName":"Corbo","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Stefano","lastName":"Barbi","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Katarzyna O.","lastName":"Sikora","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Rita T.","lastName":"Lawlor","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Giampaolo","lastName":"Tortora","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]},{"firstName":"Aldo","lastName":"Scarpa","affiliation":"University of Verona","affiliationROR":"https://ror.org/039bp8j42","affiliations":[{"name":"University of Verona","ror_id":"https://ror.org/039bp8j42"}]}],"abstract":"Background: Detection of molecular tumor heterogeneity has become of paramount importance with the advent of targeted therapies. Analysis for detection should be comprehensive, timely and based on routinely available tumor samples. Aim: To evaluate the diagnostic potential of targeted multigene next-generation sequencing (TM-NGS) in characterizing gastrointestinal cancer molecular heterogeneity. Methods: 35 gastrointestinal tract tumors, five of each intestinal type gastric carcinomas, pancreatic ductal adenocarcinomas, pancreatic intraductal papillary mucinous neoplasms, ampulla of Vater carcinomas, hepatocellular carcinomas, cholangiocarcinomas, pancreatic solid pseudopapillary tumors were assessed for mutations in 46 cancer-associated genes, using Ion Torrent semiconductor-based TM-NGS. One ampulla of Vater carcinoma cell line and one hepatic carcinosarcoma served to assess assay sensitivity. TP53, PIK3CA, KRAS, and BRAF mutations were validated by conventional Sanger sequencing. Results: TM-NGS yielded overlapping results on matched fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues, with a mutation detection limit of 1% for fresh-frozen high molecular weight DNA and 2% for FFPE partially degraded DNA. At least one somatic mutation was observed in all tumors tested; multiple alterations were detected in 20/35 (57%) tumors. Seven cancers displayed significant differences in allelic frequencies for distinct mutations, indicating the presence of intratumor molecular heterogeneity; this was confirmed on selected samples by immunohistochemistry of p53 and Smad4, showing concordance with mutational analysis. Conclusions: TM-NGS is able to detect and quantitate multiple gene alterations from limited amounts of DNA, moving one step closer to a next-generation histopathologic diagnosis that integrates morphologic, immunophenotypic, and multigene mutational analysis on routinely processed tissues, essential for personalized cancer therapy.","keywords":["Homo Sapiens","Next-generation sequencing","Personalized therapy","Molecular heterogeneity","Histopathological diagnosis"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eAVC1 Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eAVC1.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv 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class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT1_FROZEN Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT1_FROZEN.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT2_FFPE Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT2_FFPE.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT2_FROZEN Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT2_FROZEN.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT3_FFPE Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT3_FFPE.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT3_FROZEN Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT3_FROZEN.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT4_FFPE Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT4_FFPE.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT4_FROZEN Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT4_FROZEN.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT5_FFPE Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT5_FFPE.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSPT5_FROZEN Sequences file\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in tables and figures of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSPT5_FROZEN.bam\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_0_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_1_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_2.5_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_5_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_7.5_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_10_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_15_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_20_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_25_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003efrozen_dilutioncurve_50_percent_tumor\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2A of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_0_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_10_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_25_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_50_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_75_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_90_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_95_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003effpe_dilutioncurve_100_percent_hepatocarcinoma\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThese files contain next-generation sequencing data (sequencer: Ion Torrent PGM) aligned to hg19 genome used to produce the data presented in figure 2B of the manuscript.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1371/journal.pone.0104979"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2015-07-17","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.hf93m","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":1240,"downloads":399,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.gc72v"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.gc72v/versions"},"stash:version":{"href":"/api/v2/versions/105"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.gc72v/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.gc72v","id":105,"storageSize":9924107495,"relatedPublicationISSN":"2052-4463","title":"Data from: Multi-contrast submillimetric 3-Tesla hippocampal subfield segmentation protocol and dataset","authors":[{"firstName":"Jessie","lastName":"Kulaga-Yoskovitz","affiliation":"McGill University","affiliationROR":"https://ror.org/01pxwe438","affiliations":[{"name":"McGill University","ror_id":"https://ror.org/01pxwe438"}]},{"firstName":"Boris C.","lastName":"Bernhardt","affiliation":"McGill University","affiliationROR":"https://ror.org/01pxwe438","affiliations":[{"name":"McGill University","ror_id":"https://ror.org/01pxwe438"}]},{"firstName":"Seok-Jun","lastName":"Hong","affiliation":"McGill University","affiliationROR":"https://ror.org/01pxwe438","affiliations":[{"name":"McGill University","ror_id":"https://ror.org/01pxwe438"}]},{"firstName":"Tommaso","lastName":"Mansi","affiliation":"Medical Imaging Technologies, Healthcare Technology Center, Siemens Medical Solution USA, Inc., Princeton, USA","affiliations":[{"name":"Medical Imaging Technologies, Healthcare Technology Center, Siemens Medical Solution USA, Inc., Princeton, USA"}]},{"firstName":"Kevin E.","lastName":"Liang","affiliation":"McGill University","affiliationROR":"https://ror.org/01pxwe438","affiliations":[{"name":"McGill University","ror_id":"https://ror.org/01pxwe438"}]},{"firstName":"Andre J. W.","lastName":"van der Kouwe","affiliation":"Massachusetts General Hospital","affiliationROR":"https://ror.org/002pd6e78","affiliations":[{"name":"Massachusetts General Hospital","ror_id":"https://ror.org/002pd6e78"}]},{"firstName":"Jonathan","lastName":"Smallwood","affiliation":"University of York","affiliationROR":"https://ror.org/04m01e293","affiliations":[{"name":"University of York","ror_id":"https://ror.org/04m01e293"}]},{"firstName":"Andrea","lastName":"Bernasconi","affiliation":"McGill University","affiliationROR":"https://ror.org/01pxwe438","affiliations":[{"name":"McGill University","ror_id":"https://ror.org/01pxwe438"}]},{"firstName":"Neda","lastName":"Bernasconi","affiliation":"McGill University","affiliationROR":"https://ror.org/01pxwe438","affiliations":[{"name":"McGill University","ror_id":"https://ror.org/01pxwe438"}]}],"abstract":"The hippocampus is composed of distinct anatomical subregions that participate in multiple cognitive processes and are differentially affected in prevalent neurological and psychiatric conditions. Advances in high-field MRI allow for the non-invasive identification of hippocampal substructure. These approaches, however, demand time-consuming manual segmentation that relies heavily on anatomical expertise. Here, we share manual labels and associated high-resolution MRI data (MNI-HISUB25; submillimetric T1- and T2-weighted images, detailed sequence information, and stereotaxic probabilistic anatomical maps) based on 25 healthy subjects. Data were acquired on a widely available 3 Tesla MRI system using a 32 phased-array head coil. The protocol divided the hippocampal formation into three subregions: subicular complex, merged Cornu Ammonis 1, 2 and 3 (CA1-3) subfields, and CA4-dentate gyrus (CA4-DG). Segmentation was guided by consistent intensity and morphology characteristics of the densely myelinated molecular layer together with few geometry-based boundaries flexible to overall mesiotemporal anatomy, and achieved excellent intra-/inter-rater reliability (Dice index ≥90/87%). The dataset can inform neuroimaging assessments of the mesiotemporal lobe and help to develop segmentation algorithms relevant for basic and clinical neurosciences.","usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eREADME.txt\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eReadme file. Naming conventions and data info.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eexam_card_T1w_standard.pdf\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eParameters for the standard T1w sequence (1x1x1 mm^3); zipped\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eexam_card_T1wHiRes.pdf\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eParameters for the high-resolution T1w sequence (.6x.6x.6 mm^3). Two identical acquisitions of this sequence need to be made to increase SNR; zipped\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eexam_card_T2wHiRes.pdf\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eParameters for the high-resolution T2w sequence (.4x.4x2 mm^3); zipped\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eTable1_demographics_participants\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eTable 1. Participant demographics\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eTable2_CNR\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eTable 2. Contrast to noise estimates for all subjects.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eTable3_reliability\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eTable 3. Intra- and inter-rater reliability estimates.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_dataset\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eHigh-resolution 3-Tesla MRI dataset including submillimetric T1-weighted images, T2-weighted images, and subregional hippocampal labels of 25 healthy subjects. Data are provided in nifti format and both native and stereotaxic space. The dataset also includes stereotaxic probabilistic anatomical maps of all subregions.  Please see README.txt for further details.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1038/sdata.2015.59"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2016-10-22","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.gc72v","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":1324,"downloads":164,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2j27b"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2j27b/versions"},"stash:version":{"href":"/api/v2/versions/106"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2j27b/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.2j27b","id":106,"storageSize":9921153994,"relatedPublicationISSN":"0962-1083","title":"Data from: Demographic model selection using random forests and the site frequency spectrum","authors":[{"firstName":"Megan L.","lastName":"Smith","affiliation":"The Ohio State University","affiliationROR":"https://ror.org/00rs6vg23","affiliations":[{"name":"The Ohio State University","ror_id":"https://ror.org/00rs6vg23"}]},{"firstName":"Megan","lastName":"Ruffley","affiliation":"University of Idaho","affiliationROR":"https://ror.org/03hbp5t65","affiliations":[{"name":"University of Idaho","ror_id":"https://ror.org/03hbp5t65"}]},{"firstName":"Anahí","lastName":"Espindola","affiliation":"University of Idaho","affiliationROR":"https://ror.org/03hbp5t65","affiliations":[{"name":"University of Idaho","ror_id":"https://ror.org/03hbp5t65"}]},{"firstName":"David C.","lastName":"Tank","affiliation":"University of Idaho","affiliationROR":"https://ror.org/03hbp5t65","affiliations":[{"name":"University of Idaho","ror_id":"https://ror.org/03hbp5t65"}]},{"firstName":"Jack","lastName":"Sullivan","affiliation":"University of Idaho","affiliationROR":"https://ror.org/03hbp5t65","affiliations":[{"name":"University of Idaho","ror_id":"https://ror.org/03hbp5t65"}]},{"firstName":"Bryan C.","lastName":"Carstens","affiliation":"The Ohio State University","affiliationROR":"https://ror.org/00rs6vg23","affiliations":[{"name":"The Ohio State University","ror_id":"https://ror.org/00rs6vg23"}]}],"abstract":"Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the “curse of dimensionality” and issues related to the simulation and summarization of data when applied to next-generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated.","funders":[{"organization":"National Science Foundation","identifierType":"ror","identifier":"https://ror.org/021nxhr62","awardNumber":"DEB-1457726"}],"keywords":["Haplotrema vancouverense"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eBarcodes_Grp2_Mar2016_MEC-17-0128\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eBarcodes associated with Grp2_i03_Mar2016.fastq.gz.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eBarcodes_Grp1_Mar2016_MEC-17-0128\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eBarcodes associated with Grp1_i12_Mar2016.fastq.gz.\u003c/div\u003e\u003cdiv 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class=\"o-heading__level3-file-title\"\u003eGrp1_i06_Nov2015.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eGrp2_i03_Mar2016.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eGrp1_i12_Mar2016.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eGrp5_i06_Mar2016.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eGrp3_i04_Mar2016.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eGrp4_i05_Mar2016.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eZBCi12-V1T-1_S50_L006_R1_001.fastq\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eparams_ex\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eExample of a params file used in pyramid for this study.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eHaplo_July2016_77Samples_p60.unlinked_snps\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003epyRAD output: unlinked snps\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eHaplo_July2016_77Samples_p60.snps\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003epyRAD output: SNPs\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eHaplo_July2016_77Samples_p60.alleles\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003epyRAD output: alleles\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eHaplo_July2016_77Samples_p60.loci\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003epyRAD output: loci\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"North America"},{"place":"Pacific Northwest"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/mec.14223"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2017-07-21","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.2j27b","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":614,"downloads":121,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.3cb81"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.3cb81/versions"},"stash:version":{"href":"/api/v2/versions/107"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.3cb81/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.3cb81","id":107,"storageSize":9907875274,"relatedPublicationISSN":"0027-8424","title":"Data from: Directional selection can drive the evolution of modularity in complex traits","authors":[{"firstName":"Diogo","lastName":"Melo","affiliation":"Universidade de São Paulo","affiliationROR":"https://ror.org/036rp1748","affiliations":[{"name":"Universidade de São Paulo","ror_id":"https://ror.org/036rp1748"}]},{"firstName":"Gabriel","lastName":"Marroig","affiliation":"Universidade de São Paulo","affiliationROR":"https://ror.org/036rp1748","affiliations":[{"name":"Universidade de São Paulo","ror_id":"https://ror.org/036rp1748"}]}],"abstract":"Modularity is a central concept in modern biology, providing a powerful framework for the study of living organisms on many organizational levels. Two central and related questions can be posed in regard to modularity: How does modularity appear in the first place, and what forces are responsible for keeping and/or changing modular patterns? We approached these questions using a quantitative genetics simulation framework, building on previous results obtained with bivariate systems and extending them to multivariate systems. We developed an individual-based model capable of simulating many traits controlled by many loci with variable pleiotropic relations between them, expressed in populations subject to mutation, recombination, drift, and selection. We used this model to study the problem of the emergence of modularity, and hereby show that drift and stabilizing selection are inefficient at creating modular variational structures. We also demonstrate that directional selection can have marked effects on the modular structure between traits, actively promoting a restructuring of genetic variation in the selected population and potentially facilitating the response to selection. Furthermore, we give examples of complex covariation created by simple regimes of combined directional and stabilizing selection and show that stabilizing selection is important in the maintenance of established covariation patterns. Our results are in full agreement with previous results for two-trait systems and further extend them to include scenarios of greater complexity. Finally, we discuss the evolutionary consequences of modular patterns being molded by directional selection.","keywords":["variational modularity","phenotypic correlations","P-matrix","G-matrix"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eC code for the simulations\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRequires GSL to be instaled in the system. Makefile should work out of the box in unix systems.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eevomod_c_code.tar\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFirst part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003emust be joined with the other parts using cat. See dryad wiki for instructions.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsaa\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSecond part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003emust be joined with the other parts using cat. See dryad wiki for instructions.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsab\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eThird part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003emust bet joined with the other parts using cat. Se dryad wiki for instructions.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsac\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFourth part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003emust be joined with the other parts using cat. See dryad wiki for instructions.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsad\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eFifth part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsae\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSixt part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsaf\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eSeventh part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsag\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eEigth part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsah\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eNinth part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsai\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eTenth part of the main simulation results\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-name\"\u003esplit_resultsaj\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1073/pnas.1322632112"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2015-12-19","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.3cb81","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":505,"downloads":98,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.5bk4c"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.5bk4c/versions"},"stash:version":{"href":"/api/v2/versions/339300"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.5bk4c/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.5bk4c","id":108,"storageSize":9899200984,"relatedPublicationISSN":"1755-0998","title":"Data from: Gene expression differs in codominant prairie grasses under drought","authors":[{"firstName":"Ava M.","lastName":"Hoffman","email":"ava.hoffman@colostate.edu","affiliation":"Colorado State University","affiliationROR":"https://ror.org/03k1gpj17","affiliations":[{"name":"Colorado State University","ror_id":"https://ror.org/03k1gpj17"}]},{"firstName":"Melinda D.","lastName":"Smith","affiliation":"Colorado State University","affiliationROR":"https://ror.org/03k1gpj17","affiliations":[{"name":"Colorado State University","ror_id":"https://ror.org/03k1gpj17"}]},{"firstName":"Ava","lastName":"Hoffman","email":"avamariehoffman@gmail.com","affiliation":",","affiliations":[{"name":","}],"orcid":"0000-0002-1833-4397"}],"abstract":"\u003cp\u003eGrasslands of the Central US are expected to experience severe droughts and other climate extremes in the future, yet we know little about how these grasses will respond in terms of gene expression. We compared gene expression in \u003cem\u003eAndropogon gerardii\u003c/em\u003e and \u003cem\u003eSorghastrum nutans\u003c/em\u003e, two closely related co-dominant C4 grasses responsible for the majority of ecosystem function, using RNA-seq. We compared Trinity assemblies within each species to determine annotated functions of transcripts responding to drought. Subsequently, we compared homologous annotated gene-groups across the two species using cross-species meta-level analysis and functional clustering based on key terms. The majority of variation was found between species, as opposed to between drought and watered treatments. However, there is evidence for differential responses; \u003cem\u003eAndropogon\u003c/em\u003e allocated gene expression differently compared to \u003cem\u003eSorghastrum\u003c/em\u003e, suggesting \u003cem\u003eAndropogon\u003c/em\u003e focuses on stress alleviation (such as oxygen radical scavenging) rather than prevention. In contrast, Sorghastrum may employ a drought avoidance strategy by modulating osmotic response, especially with hormonal regulation. We found Sorghastrum tended to be more sensitive within 10 key gene-groups related to stress, abscisic acid, and trichomes, suggesting gene expression may mechanistically parallel sensitivity at the physiological level. Our findings corroborate phenotypic and physiological differences in the field and may help explain the phenotypic mechanisms of these two species in the tallgrass prairie community under future drought scenarios.\u003c/p\u003e","keywords":["Sorghastrum nutans","C4 grass","dominant species","comparative expression"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\n\u003ctable border=\"1\" width=\"776\" cellspacing=\"1\" cellpadding=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eraw_data\u003c/td\u003e\n\u003ctd\u003eContains raw sequence data.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTrinity_andropogon_02122016.fasta\u003c/td\u003e\n\u003ctd\u003e\n\u003cdiv class=\"o-metadata__file-description\"\u003eTrinity assembly of the \u003cem\u003eAndropogon gerardii\u003c/em\u003e transcriptome, assembled Feb 12, 2016.\u003c/div\u003e\n\u003cdiv class=\"o-metadata__file-name\"\u003e\n\u003cstrong\u003eFile:\u003c/strong\u003e Trinity_andro_out.Trinity.02122016.fasta\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTrinity_sorghastrum_04102016.fasta\u003c/td\u003e\n\u003ctd\u003e\n\u003cdiv class=\"o-metadata__file-description\"\u003eTrinity assembly of Sorghastrum nutans transcriptome, assembled Apr 10, 2016.\u003c/div\u003e\n\u003cdiv class=\"o-metadata__file-name\"\u003e\n\u003cstrong\u003eFile:\u003c/strong\u003e Trinity_sorgh_out.Trinity.fasta\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRSEM_read_counts\u003c/td\u003e\n\u003ctd\u003eRaw and TMM-normalized read counts for transcript expression prior to analysis.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eedgeR_results_andropogon\u003c/td\u003e\n\u003ctd\u003eCounts and differential expression for Andropogon.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eedgeR_results_both_species\u003c/td\u003e\n\u003ctd\u003ePreliminary count data for annotation clustered genes compared between the two species. This information was processed further in the pipeline to account for multiple copy numbers (orthologs, paralogs)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eedgeR_results_sorghastrum\u003c/td\u003e\n\u003ctd\u003eCount and differential expression data for Sorghastrum.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eKristiansson_analysis_results\u003c/td\u003e\n\u003ctd\u003eAccounts for multiple different gene copy numbers in differential expression.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eall_paralogs_bothspecies\u003c/td\u003e\n\u003ctd\u003e\n\u003cdiv class=\"o-metadata__file-description\"\u003eCounts of paralogs/orthologs found in each gene annotation group for each species, not filtered by significance.\u003c/div\u003e\n\u003cdiv class=\"o-metadata__file-name\"\u003e\n\u003cstrong\u003eFile:\u003c/strong\u003e all_paralogs_xspecies_2tail.csv\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e","locations":[{"place":"United States of America"},{"place":"Central Kansas"},{"place":"Kansas"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1111/1755-0998.12733"}],"versionNumber":4,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"metadata_changed","publicationDate":"2017-10-31","lastModificationDate":"2025-01-13","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.5bk4c","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":4813,"downloads":286,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p61vk"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p61vk/versions"},"stash:version":{"href":"/api/v2/versions/109"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p61vk/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.p61vk","id":109,"storageSize":9891854826,"relatedPublicationISSN":"0962-8452","title":"Data from: Recombination in the eggs and sperm in a simultaneously hermaphroditic vertebrate","authors":[{"firstName":"Loukas","lastName":"Theodosiou","affiliation":"Max Planck Institute for Evolutionary Biology","affiliationROR":"https://ror.org/0534re684","affiliations":[{"name":"Max Planck Institute for Evolutionary Biology","ror_id":"https://ror.org/0534re684"}]},{"firstName":"W. O.","lastName":"McMillan","affiliation":"Smithsonian Tropical Research Institute","affiliationROR":"https://ror.org/035jbxr46","affiliations":[{"name":"Smithsonian Tropical Research Institute","ror_id":"https://ror.org/035jbxr46"}]},{"firstName":"Oscar","lastName":"Puebla","affiliation":"GEOMAR Helmholtz Centre for Ocean Research Kiel","affiliationROR":"https://ror.org/02h2x0161","affiliations":[{"name":"GEOMAR Helmholtz Centre for Ocean Research Kiel","ror_id":"https://ror.org/02h2x0161"}]}],"abstract":"When there is no recombination (achiasmy) in one sex, it is in the heterogametic one. This observation is so consistent that it constitutes one of the few patterns in biology that may be regarded as a ‘rule’ and Haldane (Haldane 1922 J. Genet. 12, 101–109. (doi:10.1007/BF02983075)) proposed that it might be driven by selection against recombination in the sex chromosomes. Yet differences in recombination rates between the sexes (heterochiasmy) have also been reported in hermaphroditic species that lack sex chromosomes. In plants—the vast majority of which are hermaphroditic—selection at the haploid stage has been proposed to drive heterochiasmy. Yet few data are available for hermaphroditic animals, and barely any for hermaphroditic vertebrates. Here, we leverage reciprocal crosses between two black hamlets (Hypoplectrus nigricans, Serranidae), simultaneously hermaphroditic reef fishes from the wider Caribbean, to generate high-density egg- and sperm-specific linkage maps for each parent. We find globally higher recombination rates in the eggs, with dramatically pronounced heterochiasmy at the chromosome peripheries. We suggest that this pattern may be due to female meiotic drive, and that this process may be an important source of heterochiasmy in animals. We also identify a large non-recombining region that may play a role in speciation and local adaptation in Hypoplectrus.","keywords":["Hypoplectrus","recombination","Meiosis","hermaphrodites","heterochiasmy","Hypoplectrus nigricans"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eMap for parent 1 (both sexes)\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eLinkage map for parent 1 (both sexes)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eParent_1_both_sexes.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eMap for parent 2 (both sexes)\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eLinkage map for parent 2 (both sexes)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eParent_2_both_sexes.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eMap for parent 1 (eggs)\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eLinkage map for parent 1 (eggs)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eParent_1_eggs.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eMap for parent 1 (sperm)\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eLinkage map for parent 1 (sperm)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eParent_1_sperm.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eMap for parent 2 (eggs)\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eLinkage map for parent 2 (eggs)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eParent_2_eggs.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eMap for parent 2 (sperm)\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eLinkage map for parent 2 (sperm)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eParent_2_sperm.xlsx\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eRAD demultiplexed filtered data\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eRAD data, demultiplexed and filtered (read 1)\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eRAD_demultiplexed_filtered_data.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"Bocas del Toro"},{"place":"Panama"},{"place":"Caribbean"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1098/rspb.2016.1821"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2016-11-15","lastModificationDate":"2020-06-30","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.p61vk","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":425,"downloads":21,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2hr38"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2hr38/versions"},"stash:version":{"href":"/api/v2/versions/110"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.2hr38/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.2hr38","id":110,"storageSize":9880500050,"relatedPublicationISSN":"1544-9173","title":"Data from: Occipital alpha activity during stimulus processing gates the information flow to object-selective cortex","authors":[{"firstName":"Johanna M.","lastName":"Zumer","affiliation":"Radboud University Nijmegen","affiliationROR":"https://ror.org/016xsfp80","affiliations":[{"name":"Radboud University Nijmegen","ror_id":"https://ror.org/016xsfp80"}]},{"firstName":"René","lastName":"Scheeringa","affiliation":"Radboud University Nijmegen","affiliationROR":"https://ror.org/016xsfp80","affiliations":[{"name":"Radboud University Nijmegen","ror_id":"https://ror.org/016xsfp80"}]},{"firstName":"Jan-Mathijs","lastName":"Schoffelen","affiliation":"Radboud University Nijmegen","affiliationROR":"https://ror.org/016xsfp80","affiliations":[{"name":"Radboud University Nijmegen","ror_id":"https://ror.org/016xsfp80"},{"name":"Max Planck Institute for Psycholinguistics","ror_id":"https://ror.org/00671me87"}]},{"firstName":"David G.","lastName":"Norris","affiliation":"Radboud University Nijmegen","affiliationROR":"https://ror.org/016xsfp80","affiliations":[{"name":"Radboud University Nijmegen","ror_id":"https://ror.org/016xsfp80"}]},{"firstName":"Ole","lastName":"Jensen","affiliation":"Radboud University Nijmegen","affiliationROR":"https://ror.org/016xsfp80","affiliations":[{"name":"Radboud University Nijmegen","ror_id":"https://ror.org/016xsfp80"}]}],"abstract":"Given the limited processing capabilities of the sensory system, it is essential that attended information is gated to downstream areas, whereas unattended information is blocked. While it has been proposed that alpha band (8–13 Hz) activity serves to route information to downstream regions by inhibiting neuronal processing in task-irrelevant regions, this hypothesis remains untested. Here we investigate how neuronal oscillations detected by electroencephalography in visual areas during working memory encoding serve to gate information reflected in the simultaneously recorded blood-oxygenation-level-dependent (BOLD) signals recorded by functional magnetic resonance imaging in downstream ventral regions. We used a paradigm in which 16 participants were presented with faces and landscapes in the right and left hemifields; one hemifield was attended and the other unattended. We observed that decreased alpha power contralateral to the attended object predicted the BOLD signal representing the attended object in ventral object-selective regions. Furthermore, increased alpha power ipsilateral to the attended object predicted a decrease in the BOLD signal representing the unattended object. We also found that the BOLD signal in the dorsal attention network inversely correlated with visual alpha power. This is the first demonstration, to our knowledge, that oscillations in the alpha band are implicated in the gating of information from the visual cortex to the ventral stream, as reflected in the representationally specific BOLD signal. This link of sensory alpha to downstream activity provides a neurophysiological substrate for the mechanism of selective attention during stimulus processing, which not only boosts the attended information but also suppresses distraction. Although previous studies have shown a relation between the BOLD signal from the dorsal attention network and the alpha band at rest, we demonstrate such a relation during a visuospatial task, indicating that the dorsal attention network exercises top-down control of visual alpha activity.","keywords":["dorsal attention network","EEG-fMRI","alpha","gating","Attention","oscillations"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject01\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f01.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject02\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f02.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject03\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f03.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject04\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f04.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject05\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f05.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject06\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f06.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject07\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f07.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject08\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f08.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject09\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f09.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject10\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f10.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject11\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f11.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject12\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f12.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject13\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f13.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject14\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f14.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject15\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f15.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eeeg_subject16\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eEEG data from inside the MRI.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eraw_resamp_notrej_f16.mat\u003c/br\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003ebehav_electrodepos\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eFiles for all subjects for EEG electrode positions, output from Presentation scripts, and eye-tracker information.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f01\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f02\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f03\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f04\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f05\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f06\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f07\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f08\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f09\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f10\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f11\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f12\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f13\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f14\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f15\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003emri_f16\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eAll MRI data for each participant separately.  (Structural, functional main task, and functional localizer task).\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1371/journal.pbio.1001965"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2015-09-17","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.2hr38","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":1876,"downloads":887,"citations":2}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p8br3r4"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p8br3r4/versions"},"stash:version":{"href":"/api/v2/versions/111"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.p8br3r4/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.p8br3r4","id":111,"storageSize":9876101678,"relatedPublicationISSN":"0962-8452","title":"Data from: Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp","authors":[{"firstName":"Zhiguang","lastName":"Qiu","affiliation":"UNSW Sydney","affiliationROR":"https://ror.org/03r8z3t63","affiliations":[{"name":"UNSW Sydney","ror_id":"https://ror.org/03r8z3t63"}]},{"firstName":"Melinda A","lastName":"Coleman","affiliation":"Department of Natural Resources and Environment Tasmania","affiliationROR":"https://ror.org/00jwytm78","affiliations":[{"name":"Department of Natural Resources and Environment Tasmania","ror_id":"https://ror.org/00jwytm78"}]},{"firstName":"Euan","lastName":"Provost","affiliation":"Southern Cross University","affiliationROR":"https://ror.org/001xkv632","affiliations":[{"name":"Southern Cross University","ror_id":"https://ror.org/001xkv632"}]},{"firstName":"Alexandra H","lastName":"Campbell","affiliation":"UNSW Sydney","affiliationROR":"https://ror.org/03r8z3t63","affiliations":[{"name":"UNSW Sydney","ror_id":"https://ror.org/03r8z3t63"}]},{"firstName":"Brendan P","lastName":"Kelaher","affiliation":"Southern Cross University","affiliationROR":"https://ror.org/001xkv632","affiliations":[{"name":"Southern Cross University","ror_id":"https://ror.org/001xkv632"}]},{"firstName":"Steven J","lastName":"Dalton","affiliation":"Southern Cross University","affiliationROR":"https://ror.org/001xkv632","affiliations":[{"name":"Southern Cross University","ror_id":"https://ror.org/001xkv632"}]},{"firstName":"Torsten","lastName":"Thomas","affiliation":"UNSW Sydney","affiliationROR":"https://ror.org/03r8z3t63","affiliations":[{"name":"UNSW Sydney","ror_id":"https://ror.org/03r8z3t63"}]},{"firstName":"Peter D","lastName":"Steinberg","affiliation":"UNSW Sydney","affiliationROR":"https://ror.org/03r8z3t63","affiliations":[{"name":"UNSW Sydney","ror_id":"https://ror.org/03r8z3t63"}]},{"firstName":"Ezequiel M","lastName":"Marzinelli","affiliation":"UNSW Sydney","affiliationROR":"https://ror.org/03r8z3t63","affiliations":[{"name":"UNSW Sydney","ror_id":"https://ror.org/03r8z3t63"}]}],"abstract":"Climate change is driving global declines of marine habitat-forming species through physiological effects and through changes to ecological interactions, with projected trajectories for oceanwarming and acidification likely to exacerbate such impacts in coming decades. Interactions between habitat-formers and their microbiomes are fundamental for host functioning and resilience, but how such relationships will change in future conditions is largely unknown. We investigated independent and interactive effects of warming and acidification on a large brown seaweed, the kelp Ecklonia radiata, and its associated microbiome in experimental mesocosms. Microbial communities were affected by warming and, during the first week, by acidification. During the second week, kelp developed disease-like symptoms previously observed in the field. The tissue of some kelp blistered, bleached and eventually degraded, particularly under the acidification treatments, affecting photosynthetic efficiency. Microbial communities differed between blistered and healthy kelp for all treatments, except for those under future conditions of warming and acidification, which after two weeks resembled assemblages associated with healthy hosts. This indicates that changes in the microbiome were not easily predictable as the severity of future climate scenarios increased. Future ocean conditions can change kelp microbiomes and may lead to host disease, with potentially cascading impacts on associated ecosystems.","keywords":["Ecklonia radiata","disease","holobiont","acidification"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eAmplicon sequences of microbial communities on the surface of ecklonia radiata under different environmental conditions\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThis data includes bacterial 16S rRNA gene sequences of microbial communities on the surface of Ecklonia radiata under different environmental scenarios. Amplicon sequencing used 27F/519R primers contain V1-V3 regions.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eSubmission_RSPB-2018-0507.tar\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1098/rspb.2018.1887"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2019-04-08","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.p8br3r4","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":361,"downloads":27,"citations":1}},{"_links":{"self":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.3528sj6"},"stash:versions":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.3528sj6/versions"},"stash:version":{"href":"/api/v2/versions/112"},"stash:download":{"href":"/api/v2/datasets/doi%3A10.5061%2Fdryad.3528sj6/download"},"curies":[{"name":"stash","href":"https://github.com/datadryad/dryad-app/blob/main/documentation/apis/link_relations.md#{rel}","templated":"true"}]},"identifier":"doi:10.5061/dryad.3528sj6","id":112,"storageSize":9858763736,"relatedPublicationISSN":"2399-3421","title":"Data from: Opening the door to the past: accessing phylogenetic, pathogen, and population data from museum curated bees","authors":[{"firstName":"Anthony D.","lastName":"Vaudo","affiliation":"Pennsylvania State University","affiliationROR":"https://ror.org/04p491231","affiliations":[{"name":"Pennsylvania State University","ror_id":"https://ror.org/04p491231"}]},{"firstName":"Megan L.","lastName":"Fritz","affiliation":"University of Maryland, College Park","affiliationROR":"https://ror.org/047s2c258","affiliations":[{"name":"University of Maryland, College Park","ror_id":"https://ror.org/047s2c258"}]},{"firstName":"Margarita M.","lastName":"López-Uribe","affiliation":"Pennsylvania State University","affiliationROR":"https://ror.org/04p491231","affiliations":[{"name":"Pennsylvania State University","ror_id":"https://ror.org/04p491231"}]}],"abstract":"Tens of thousands of insects are deposited in collections every year as a result of survey-based studies that aim to investigate ecological questions. DNA-based techniques can expand the utility of these collections to explore their demographic and evolutionary history, temporal changes in their abundance, and pathogen dynamics. Using museum collections of the non-model bee species Eucera (Peponapis) pruinosa Say 1837 (Hymenoptera: Apidae: Eucerini), we developed a standard minimally-destructive and budget-friendly protocol to extract DNA and amplify common gene-fragments for barcoding, phylogenetic analysis, and pathogens. We also generated genome-wide single nucleotide polymorphism (SNP) data from DNA sequencing (ddRADseq) libraries for population structure analyses. We systematically studied the effect of specimen age (≤10 years ago) and tissue type (whole bees vs. abdomen) on DNA quality, single gene-fragment amplification, and SNP calling. We found that all analyses were achievable with both tissue types, yet with variable levels of efficiency because of general DNA degradation. Specifically, we found that not all samples yielded satisfactory results for molecular studies; however, we did not find a systematic effect of specimen age on DNA quality which is encouraging for future studies involving historical specimens. We report the first evidence for the presence of the microsporidian pathogen Nosema spp. in squash bees, opening a window for the study of historical changes in disease pressure in this important agricultural pollinator. Our protocols can be used as a template for the design of future experiments that extract multiple pieces of information using DNA-based methods from insect museum stored specimens.","keywords":["Eucera (Peponapis) pruinosa"],"usageNotes":"\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eREADME\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eddRAD sequences and metadata from Eucera (Peponapis) pruinosa (Hymenoptera: Apidae) males used in: \r\n\r\nAnthony D. Vaudo, Megan L. Fritz, and Margarita  M. Lopez-Uribe (2018) Opening the door to the past: Accessing phylogenetic, pathogen, and population data from museum curated bees. Insect Systematics and Diversity\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"o-metadata__file-usage-entry\"\u003e\u003ch4 class=\"o-heading__level3-file-title\"\u003eEucera pruinosa museum specimen ddRAD reads\u003c/h4\u003e\u003cdiv class=\"o-metadata__file-description\"\u003eThis file contains the demultiplexed, trimmed and quality filtered ddRAD reads from museum curated Eucera (Peponapis) pruinosa males. See README file for specific information about sample metadata.\u003c/div\u003e\u003cdiv class=\"o-metadata__file-name\"\u003eallmuseum_merged_edits.zip\u003c/br\u003e\u003c/div\u003e\u003c/div\u003e","locations":[{"place":"Central Pennsylvania"}],"relatedWorks":[{"relationship":"primary_article","identifierType":"DOI","identifier":"https://doi.org/10.1093/isd/ixy014"}],"versionNumber":1,"versionStatus":"submitted","curationStatus":"Published","versionChanges":"none","publicationDate":"2018-10-16","lastModificationDate":"2020-06-24","visibility":"public","sharingLink":"http://datadryad.org/dataset/doi:10.5061/dryad.3528sj6","license":"https://spdx.org/licenses/CC0-1.0.html","metrics":{"views":279,"downloads":8,"citations":1}}]}}