{"data":[{"id":"10.5281/zenodo.22979178","type":"dois","attributes":{"doi":"10.5281/zenodo.22979178","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Exeter"],"givenName":"Yiyang","familyName":"Tan","name":"Tan, Yiyang","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["East China Normal University"],"givenName":"Zhixuan","familyName":"Feng","name":"Feng, Zhixuan","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0002-4774-7027"}]},{"nameType":"Personal","affiliation":["Norwegian Institute for Water Research","University of Oslo Centre for Ecological and Evolutionary Synthesis"],"givenName":"Kristina Øie","familyName":"Kvile","name":"Kvile, Kristina Øie","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0003-2771-9077"}]}],"titles":[{"title":"Standardized Calanus glacialis and Calanus hyperboreus Abundance Data with Spatiotemporally Matched Environmental Variables"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Calanoid copepods"},{"subject":"habitat suitability"},{"subject":"ecological niche modeling"},{"subject":"Arctic Ocean"}],"contributors":[],"dates":[{"date":"2026-09-26","dateType":"Issued"},{"date":"2026-10-01","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":""},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.22979179","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Overview\n\nThis dataset contains standardized abundance records of the pre-diapause C1–C2 copepodite stages of Calanus glacialis and Calanus hyperboreus across Arctic and sub-Arctic regions between 55°N and 90°N from 1993 to 2022.Biological observations were matched with environmental variables according to sampling location and date. The dataset was prepared for ecological niche modelling and habitat suitability reconstruction of Arctic Calanus species.\n\nFile included\n\nCgla_Chyp_c1c2_EnviVar.xlsxThe workbook contains standardized C1–C2 abundance records of Calanus glacialis and Calanus hyperboreus together with environmental covariates matched to each biological sampling record.\n\nVariable description\n\nlat: Sampling latitudelon: Sampling longitudeyear: Sampling yearmonth: Sampling monthday: Sampling daySIC: Sea-ice concentrationCHL: Surface chlorophyll-a concentrationSST: Sea-surface temperatureSal: Sea-surface salinityu: Zonal ocean-current velocityv: Meridional ocean-current velocitynpp: Net primary productionnitrat: Nitrate concentrationSIT: Sea-ice thicknessmlotst_glor: Mixed-layer depthCgla_c1c2: Standardized abundance of Calanus glacialis C1–C2 stagesChyp_c1c2: Standardized abundance of Calanus hyperboreus C1–C2 stagesMLDaverCHL: Mixed-layer-averaged chlorophyll-a concentrationMLDaverSal: Mixed-layer-averaged salinityMLDaverSST: Mixed-layer-averaged temperaturep60dmSIC: Mean sea-ice concentration during the 60 days preceding sampling\n\nData preparation\n\nBiological abundance records were standardized to reduce inconsistencies associated with sampling depth, sampling gear and life-stage reporting.Environmental variables were matched to each biological observation using sampling coordinates and date. 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The dataset was prepared for ecological niche modelling and habitat suitability reconstruction of Arctic Calanus species.\n\nFile included\n\nCgla_Chyp_c1c2_EnviVar.xlsxThe workbook contains standardized C1–C2 abundance records of Calanus glacialis and Calanus hyperboreus together with environmental covariates matched to each biological sampling record.\n\nVariable description\n\nlat: Sampling latitudelon: Sampling longitudeyear: Sampling yearmonth: Sampling monthday: Sampling daySIC: Sea-ice concentrationCHL: Surface chlorophyll-a concentrationSST: Sea-surface temperatureSal: Sea-surface salinityu: Zonal ocean-current velocityv: Meridional ocean-current velocitynpp: Net primary productionnitrat: Nitrate concentrationSIT: Sea-ice thicknessmlotst_glor: Mixed-layer depthCgla_c1c2: Standardized abundance of Calanus glacialis C1–C2 stagesChyp_c1c2: Standardized abundance of Calanus hyperboreus C1–C2 stagesMLDaverCHL: Mixed-layer-averaged chlorophyll-a concentrationMLDaverSal: Mixed-layer-averaged salinityMLDaverSST: Mixed-layer-averaged temperaturep60dmSIC: Mean sea-ice concentration during the 60 days preceding sampling\n\nData preparation\n\nBiological abundance records were standardized to reduce inconsistencies associated with sampling depth, sampling gear and life-stage reporting.Environmental variables were matched to each biological observation using sampling coordinates and date. 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By integrating controlled experiments with analytical and computational modeling, the project will advance understanding of the mechanics of segmented hard-on-soft systems, and provide a mechanics-based foundation for future development of materials that combine flexibility with protection. The project will generate geometric measurements and computer-aided design models, material fabrication and testing protocols, images and full-field deformation data, constituent and interfacial characterization data, structural-response measurements, analytical and computational models, model inputs, and simulation outputs. These data will support model calibration and validation, guide the selection of test conditions, quantify geometry-material interactions, link measured material behavior to structural response, and inform the mechanism map.","lang":"en"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Science Foundation (nsf.gov)","funderIdentifier":"https://ror.org/021nxhr62"}],"url":"https://dmphub.uc3prd.cdlib.net/dmps/10.48321/D12C21A5E4","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-27T19:19:27Z","registered":"2026-09-27T19:19:28Z","published":null,"updated":"2026-09-27T19:19:28Z"},"relationships":{"client":{"data":{"id":"cdl.cdl","type":"clients"}}}},{"id":"10.17863/cam.79841","type":"dois","attributes":{"doi":"10.17863/cam.79841","identifiers":[{"identifier":"PMC8608917","identifierType":"OTHER"},{"identifier":"34811357","identifierType":"OTHER"}],"creators":[{"name":"Liu, Wei","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4071-6017"}],"affiliation":[]},{"name":"Wang, Xiting","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5905-7285"}],"affiliation":[]},{"name":"Wang, Fan","affiliation":[],"nameIdentifiers":[]},{"name":"Du, Kaifa","affiliation":[],"nameIdentifiers":[]},{"name":"Zhang, Zhaofu","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1406-1256"}],"affiliation":[]},{"name":"Guo, Yuzheng","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9224-3816"}],"affiliation":[]},{"name":"Yin, Huayi","affiliation":[],"nameIdentifiers":[]},{"name":"Wang, Dihua","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2364-8718"}],"affiliation":[]}],"titles":[{"title":"A durable and pH-universal self-standing MoC-Mo2C heterojunction electrode for efficient hydrogen evolution reaction."}],"publisher":"Springer Nature","container":{},"publicationYear":2021,"subjects":[{"subject":"3403 Macromolecular and Materials Chemistry"},{"subject":"34 Chemical Sciences"},{"subject":"40 Engineering"},{"subject":"4016 Materials Engineering"},{"subject":"4018 Nanotechnology"}],"contributors":[{"name":"Apollo - University of Cambridge Repository","contributorType":"DataManager","affiliation":[],"nameIdentifiers":[]},{"name":"University of Cambridge","nameIdentifiers":[{"nameIdentifierScheme":"ROR","schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/013meh722"}],"contributorType":"HostingInstitution","affiliation":[]}],"dates":[{"date":"2022-01-07","dateType":"Issued"},{"date":"2022-01-07","dateType":"Available"},{"date":"2021-11-22","dateType":"Issued"},{"date":"2022-01-07","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"JournalArticle","citeproc":"article-journal","bibtex":"article","ris":"JOUR","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsUri":"http://purl.org/coar/access_right/c_abf2","rights":"open.access"}],"descriptions":[{"descriptionType":"Abstract","description":"Efficient water electrolyzers are constrained by the lack of low-cost and earth-abundant hydrogen evolution reaction (HER) catalysts that can operate at industry-level conditions and be prepared with a facile process. Here we report a self-standing MoC-Mo2C catalytic electrode prepared via a one-step electro-carbiding approach using CO2 as the feedstock. The outstanding HER performances of the MoC-Mo2C electrode with low overpotentials at 500 mA cm-2 in both acidic (256 mV) and alkaline electrolytes (292 mV), long-lasting lifetime of over 2400 h (100 d), and high-temperature performance (70 oC) are due to the self-standing hydrophilic porous surface, intrinsic mechanical strength and self-grown MoC (001)-Mo2C (101) heterojunctions that have a ΔGH* value of -0.13 eV in acidic condition, and the energy barrier of 1.15 eV for water dissociation in alkaline solution. The preparation of a large electrode (3 cm × 11.5 cm) demonstrates the possibility of scaling up this process to prepare various carbide electrodes with rationally designed structures, tunable compositions, and favorable properties."}],"geoLocations":[],"fundingReferences":[{"funderName":"National Natural Science Foundation of China (National Science Foundation of China)","awardNumber":"52031008, 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Analysis code uses MIT; derived tables and figures use CC BY 4.0; source data retain their original repository terms."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"Crossref Funder ID","funderName":"National Natural Science Foundation of China","funderIdentifier":"10.13039/501100001809","awardNumber":"82402902"}],"url":"https://zenodo.org/doi/10.5281/zenodo.21413645","contentUrl":null,"metadataVersion":4,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":4,"versionOfCount":1,"created":"2026-07-17T12:58:44Z","registered":"2026-07-17T12:58:44Z","published":null,"updated":"2026-09-27T17:16:34Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.22999821","type":"dois","attributes":{"doi":"10.5281/zenodo.22999821","identifiers":[{"identifier":"oai:zenodo.org:22999821","identifierType":"oai"},{"identifier":"https://github.com/he544878915-stack/ScarVCell","identifierType":"URL"}],"creators":[{"nameType":"Personal","affiliation":["Ruijin Hospital, Shanghai Jiao Tong University School of Medicine"],"givenName":"Junwei","familyName":"He","name":"He, Junwei","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0008-7690-9452"}]},{"nameType":"Personal","affiliation":["Donghua University"],"givenName":"Zezhao","familyName":"Ding","name":"Ding, Zezhao","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Ruijin Hospital"],"givenName":"Wei","familyName":"Xu","name":"Xu, Wei","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Ruijin Hospital"],"givenName":"Lei","familyName":"Yi","name":"Yi, Lei","nameIdentifiers":[]}],"titles":[{"title":"FSSI-7: interpreting fibroblast-state signals in pathological scars"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"pathological scarring"},{"subject":"keloid"},{"subject":"hypertrophic scar"},{"subject":"fibroblast"},{"subject":"transcriptomics"},{"subject":"public data"},{"subject":"reproducible research"}],"contributors":[],"dates":[{"date":"2026-09-27","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"SoftwareSourceCode","resourceTypeGeneral":"Software","citeproc":"article","bibtex":"misc","ris":"COMP","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21413645","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"2.0.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rights":"MIT License","rightsIdentifier":"mit"}],"descriptions":[{"descriptionType":"Abstract","description":"Code and processed-data release accompanying Biopsy context and molecular response shape a seven-gene fibroblast-state signal in pathological scars. Includes the fixed seven-gene specification, analytical-unit results, dataset audit, calibration and evidence-role documentation, seven main figures and eight supplementary figures. A workbook-backed Python runner reconstructs 16 result tables and checks 12 expected tables for fixed development calibration, RUNX2, corin and paired chronic-keloid analyses. This is processed-input reconstruction, not raw sequencing QC, exhaustive gene-selection or full atlas-pipeline reproduction. Analysis code uses MIT; derived tables and figures use CC BY 4.0; source data retain their original repository terms."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"Crossref Funder ID","funderName":"National Natural Science Foundation of China","funderIdentifier":"10.13039/501100001809","awardNumber":"82402902"}],"url":"https://zenodo.org/doi/10.5281/zenodo.22999821","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-09-27T17:16:33Z","registered":"2026-09-27T17:16:33Z","published":null,"updated":"2026-09-27T17:16:33Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.17863/cam.82541","type":"dois","attributes":{"doi":"10.17863/cam.82541","identifiers":[{"identifier":"pel212275","identifierType":"OTHER"}],"creators":[{"name":"Chen, Xiaoyuan","affiliation":[],"nameIdentifiers":[]},{"name":"Gou, Huayu","affiliation":[],"nameIdentifiers":[]},{"name":"Chen, Yu","affiliation":[],"nameIdentifiers":[]},{"name":"Zeng, Lei","affiliation":[],"nameIdentifiers":[]},{"name":"Zhang, Mingshun","affiliation":[],"nameIdentifiers":[]},{"name":"Jiang, Shan","affiliation":[],"nameIdentifiers":[]},{"name":"Xie, Qi","affiliation":[],"nameIdentifiers":[]},{"name":"Shen, Boyang","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"An ultra‐low‐loss superconducting inductor for power electronic circuits"}],"publisher":"Institution of Engineering and Technology (IET)","container":{},"publicationYear":2022,"subjects":[{"subject":"40 Engineering"},{"subject":"4008 Electrical Engineering"}],"contributors":[{"name":"Apollo - University of Cambridge Repository","contributorType":"DataManager","affiliation":[],"nameIdentifiers":[]},{"name":"University of Cambridge","nameIdentifiers":[{"nameIdentifierScheme":"ROR","schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/013meh722"}],"contributorType":"HostingInstitution","affiliation":[]}],"dates":[{"date":"2022-03-17","dateType":"Issued"},{"date":"2022-03-17","dateType":"Available"},{"date":"2022-08","dateType":"Issued"},{"date":"2021-05-29","dateType":"Submitted"},{"date":"2022-03-17","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"JournalArticle","citeproc":"article-journal","bibtex":"article","ris":"JOUR","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[{"descriptionType":"Abstract","description":"Abstract To break through the bottlenecks of the loss and size of conventional conductors and magnetic materials, replacing copper inductors by zero‐resistance superconducting inductors can be a promising solution. The experimental, theoretical and numerical investigations into the loss characteristics of an air‐core superconducting inductor have been carried out. All these results show that the loss from a superconducting inductor in the boost chopper circuit was reduced remarkably up to 373 times smaller than the loss from the magnetic‐core copper inductor having the same conditions. Moreover, the actual volume and weight of the superconducting inductor are only 5.61% and 10.97% of those of the copper inductor having the same inductance and current capacities. Advanced numerical models have been built to verify the experiments that their results of superconducting losses as well as the current and frequency dependencies are well matched. A compact, low‐loss and low‐cost cryostat has been designed to accommodate the superconducting inductor, which can further improve the practicability for superconducting inductor to be equipped into various power electronic applications. The superconducting inductor can lead power electronic devices towards ultra‐high‐efficiency power conversion."}],"geoLocations":[],"fundingReferences":[{"funderName":"National Natural Science Foundation of China","awardNumber":"51807128"}],"url":"https://www.repository.cam.ac.uk/handle/1810/335099","contentUrl":null,"metadataVersion":20,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-03-17T12:01:58Z","registered":"2022-03-17T12:07:42Z","published":null,"updated":"2026-09-27T14:46:56Z"},"relationships":{"client":{"data":{"id":"bl.cam","type":"clients"}}}},{"id":"10.5061/dryad.g79cnp65h","type":"dois","attributes":{"doi":"10.5061/dryad.g79cnp65h","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Sapci, Ali Osman Berk","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4396-817X"}]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Arasti, Shayesteh","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Florida"],"name":"Braun, Edward","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Mirarab, Siavash","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5410-1518"}]}],"titles":[{"title":"Data from: Phlag: Scalable detection of genomics regions with unexplained phylogenetic heterogeneity"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Computer and information sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Phylogenetics","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Hidden Markov models","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Evolutionary biology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Computational biology","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Sapci, Ali Osman Berk","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4396-817X"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Mirarab, Siavash","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5410-1518"}],"contributorType":"ContactPerson"},{"name":"San Diego Supercomputer Center","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-06-22T00:36:37Z","dateType":"Created"},{"date":"2026-06-22T00:39:44Z","dateType":"Submitted"},{"date":"2026-07-13T00:00:00Z","dateType":"Issued"},{"date":"2026-07-13T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5281/zenodo.19713363","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.64898/2026.04.10.717778","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5281/zenodo.19713355","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5281/zenodo.19713368","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.20244401","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/bioinformatics/btag273","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["8806917567 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Phylogenetic analyses of entire genomes (phylogenomics) have revealed\n abundant heterogeneity of evolutionary histories. While much has been done\n to model this heterogeneity and to infer species trees despite it, the\n current toolkit has a limitation. Most methods assume that gene trees\n across the genome differ but are all sampled from the same distribution,\n defined by models such as the multi-species coalescent (MSC), and\n parametrized consistently across the genome. Empirical data strongly\n suggest this assumption is often violated because the species tree, its\n parameters, or the process generating the gene trees can all change across\n the genome. Errors in the data can further compound this heterogeneity. To\n address this challenge, we define the problem of detecting what segments\n of the genome are inconsistent with a putative species tree, even after\n allowing discordance according to MSC. We model gene trees not as a set,\n but rather as a series (a realization of a stochastic process) along\n genomic positions. We propose a Hidden Markov Model (HMM) approach applied\n to quartet statistics measured from gene trees and tie the model to MSC\n using simulations. The combined use of these three ideas leads to a\n scalable method called Phlag. On simulated and real data, we show that\n Phlag can detect many cases of change in underlying evolutionary\n processes, including reduced recombination rates, population size changes,\n and admixture, all using the same algorithm."},{"descriptionType":"TechnicalInfo","description":"# Data from: Phlag: Scalable detection of genomics regions with\n unexplained phylogenetic heterogeneity Dataset DOI:\n [10.5061/dryad.g79cnp65h](https://doi.org/10.5061/dryad.g79cnp65h) This\n repository contains the Phlag benchmarking results, gene trees, species\n trees, and supporting data for the avian dataset ([experiment\n E3](https://doi.org/10.5281/zenodo.19713363)) and mammalian dataset\n ([experiment E4](https://doi.org/10.5281/zenodo.19713368)), together with\n the simulated ARGs ([experiments E1 and\n E2](https://doi.org/10.5281/zenodo.19713355)). For the individual dataset\n (excluding ARGs), please see: * gene trees simulated using msprime and\n simulation experiments: [phlag-avian-simulations]\n [GitHub](https://github.com/bo1929/phlag-avian-simulations) and\n [Zenodo](https://doi.org/10.5281/zenodo.19713355), * analysis conducted on\n the mammalian phylogeny: [phlag-mammalian-analysis]\n [GitHub](https://github.com/bo1929/phlag-mammalian-analysis) and\n [Zenodo](https://doi.org/10.5281/zenodo.19713368), * experiments on the\n Stiller2024 avian phylogeny: [phlag-avian-analysis]\n [GitHub](https://github.com/bo1929/phlag-avian-analysis) and\n [Zenodo](https://doi.org/10.5281/zenodo.19713363). ## Mammalian analysis\n (`analysis-mammals.tar.gz`) A dataset consisting of 19,465 gene trees\n inferred from chromosome 3 of the mammalian genome alignment by Foley et\n al., covering 241 species. Gene trees were estimated by selecting 1Kbp\n subalignments with minimum missing data from each 10Kbp segment and\n running IQ-TREE under GTR+G4. Phlag was applied to 136 internal branches\n under key mammalian orders (Carnivora, Chiroptera, Primates, Artiodactyla,\n and Rodentia), retaining branches that define a quadripartition in at\n least 90% of the gene trees. Each branch was analyzed individually (single\n focal branch) using the prior-updated mode with topology-order emissions.\n ### Data * `alltrees.tree.gz`: Compressed gene trees (19,465 trees)\n inferred from chromosome 3 of the mammalian alignment by [Foley et\n al.](https://doi.org/10.1126/science.abn7829) We estimated gene trees\n using IQ-TREE under the GTR+G4 model from 1Kbp subalignments selected with\n minimum missing data from each 10Kbp segment. *\n `labelled_species_tree.nwk`: Species tree in Newick format with 241\n mammalian taxa and internal nodes labelled `I0`–`I239` with branch\n lengths. * `ref.topology`: Reference species tree topology. * `qqs.txt`:\n Precomputed quartet-quartet site (QQS) frequencies for all gene trees and\n internal branches, used as input to Phlag. * `pos`: Genomic positions (on\n the human chromosome 3 coordinate) for each gene tree window. *\n `order.txt`: File identifiers for each gene tree locus/window. *\n `taxon_map.txt`: Mapping from internal node labels (`I0`–`I239`) to\n taxonomic family names via NCBI taxonomy. * `taxon_map_order.txt`: Mapping\n from internal node labels to taxonomic order names. * `taxdump.tar.gz`:\n NCBI taxonomy database dump used by `map_lca.py`. * `map_lca.py`: Python\n script (using ete3) for resolving internal node labels to their lowest\n common ancestor in NCBI taxonomy. * `prep.sh`: Preprocessing script that\n extracts Hellinger distances from Phlag prediction files and produces\n summary files. * `echo_cmd.sh`: Script containing the Phlag commands used\n to generate predictions under different hyperparameter settings. ### Phlag\n predictions * `all_pred-{PARAMS}-chr3/`: Directories containing Phlag\n output for chromosome 3 under different hyperparameter combinations. Each\n directory contains: * `distances_chr3.txt`: Hellinger distance between the\n null and alternative emission distributions for each internal branch. *\n `pred-I{NODE}-{PARAMS}.txt`: Per-branch prediction file containing the\n Phlag command, the modified species tree, decoded state predictions, and\n the distance metric. The naming convention encodes hyperparameters as\n follows: * `eap{beta}`: expected number of anomalies\n (`--expected-num-anomalies`), e.g., `eap50`: beta = 50. * `ep{1-rho}`:\n expected anomaly proportion (`--expected-anomaly-proportion`), e.g.,\n `ep005`: 1-rho = 0.05. * `penalty{lambda}` / `npenalty{N}`: prior penalty\n strength (`lambda`), with `n` prefix indicating a negative value.\n Available parameter combinations: * `all_pred-eap50_ep005_penalty15-chr3`\n * `all_pred-eap50_ep002_penalty15-chr3` *\n `all_pred-eap40_ep005_penalty15-chr3` *\n `all_pred-eap100_ep005_penalty15-chr3` (the main paper analysis) *\n `all_pred-eap100_ep010_penalty15-chr3` ## Avian analysis\n (`analysis-avian.tar.gz`) We applied Phlag on 39,849 gene trees sampled\n from five macrochromosomes (chr1–chr5) of the 363-taxon avian dataset by\n Stiller et al. Gene trees were estimated by selecting 1Kbp subalignments\n with minimum missing data from each 10Kbp segment and running IQ-TREE\n under GTR+G4. We focused on 20 branches near the base of Neoaves, selected\n from 33 basal branches (60–68 Mya) highlighted by Stiller et al. that had\n quartet support below 0.5. Each branch was analyzed individually (single\n focal branch) using the prior-updated mode with topology-order emissions.\n ### Data * `sorted_genetrees/`: Compressed gene trees for each chromosome,\n sorted by genomic position. *\n `gene_trees-Stiller2024-chr{CHR}-sorted.nwk.gz`: Gene trees from the\n 363-taxon avian dataset by [Stiller et\n al.](https://doi.org/10.1038/s41586-024-07323-1), selecting 1Kbp\n subalignments with minimum missing data from each 10Kbp segment. Gene\n trees were estimated using IQ-TREE under the GTR+G4 model with approximate\n Bayesian support. * Covers 28 autosomes (chr1–chr28) and the Z chromosome\n (chrZ). * `qqs/`: Precomputed quartet-quartet site (QQS) frequencies, used\n as input to Phlag. * `gene_trees-Stiller2024-chr{CHR}-sorted-qqs.txt.gz`:\n Per-chromosome QQS frequencies. * `qqs-chr1-5.txt.gz`: Concatenated QQS\n for the five macrochromosomes (chr1–chr5). * `qqs-chr1-5-Z.txt.gz`:\n Concatenated QQS for the five macrochromosomes plus the Z chromosome. *\n `coordinates/`: Coordinate files for each chromosome assembly. *\n `coordinates-Stiller2024-chr{CHR}.txt`: Genomic coordinates for gene tree\n loci. * `positions-gene_trees-Stiller2024-chr{CHR}-sorted.txt`: Genomic\n positions (chromosome and base pair coordinate) for each gene tree window.\n * Species trees: * `main.tre`: Main avian species tree with CU branch\n lengths (363 taxa; Stiller et al.). * `main_alternative_mod.tre`: Modified\n species tree. * `main-num_generations.tre`: Species tree with branch\n lengths in number of generations. * `63K_dated.tre` / `2023-04-dated.tre`:\n Dated species trees with branch lengths in time (million years). *\n `castlespro_stiller.rooted.tre`: Species tree with branch lengths in\n substitution units, estimated by CASTLES-pro. * Flagged regions (Z\n chromosome analysis): *\n `flagged_regions-Stiller2024-chrZ-species_tree.nwk`: Species tree used for\n flagged Z chromosome analysis. *\n `flagged_gene_trees-Stiller2024-chrZ-sorted.nwk`: Gene trees from flagged\n regions of the Z chromosome. *\n `main_species_tree-Stiller2024-chrZ_flagged.nwk.support`: Species tree\n with support values from flagged Z chromosome gene trees. *\n `mapping_stiller_fig2a.tsv` / `mapping_stiller_main.tsv` /\n `mapping_stiller_merged.tsv`: Mapping from internal node labels (`N{ID}`)\n to branch numbers used in Stiller et al. Fig. 2a and in the paper figures.\n * `list_chr.txt`: List of all 34 chromosome/scaffold identifiers in the\n assembly. * `plot.R`: R script (using ggplot2) for generating\n visualization plots from Phlag predictions. ### Phlag predictions Phlag\n was run on individual chromosomes and on concatenations of gene trees\n across the five macrochromosomes (chr1–chr5), with and without the Z\n chromosome. Each prediction directory contains: * `distances_chr{CHR}.txt`\n / `distances_concat.txt`: Hellinger distance between the null and\n alternative emission distributions for each branch. *\n `preds-chr{CHR}-{PARAMS}.txt` / `preds-concat-{PARAMS}.txt`: Tab-separated\n prediction matrix with one row per gene tree and one column per branch.\n Values are binary (0 = null, 1 = alternative, nan = branch not present in\n the gene tree). The naming convention encodes hyperparameters as follows:\n * `eap{beta}`: expected number of anomalies (`--expected-num-anomalies`),\n e.g., `eap40`: beta = 40. * `ep{1-rho}`: expected anomaly proportion\n (`--expected-anomaly-proportion`), e.g., `ep005`: 1-rho = 0.05. *\n `penalty{lambda}` / `npenalty{lambda}`: prior penalty strength (`lambda`),\n with `n` prefix indicating a negative value. * `noprior`: segment mode (no\n MSC-based prior on emissions). Available parameter combinations\n (per-chromosome predictions): * `eap40_ep005_penalty15/` (prior-updated,\n the main paper analysis) * `eap40_ep005_noprior/` (segment mode) *\n `eap40_ep005_npenalty15/` (negative penalty) Concatenated analyses: *\n `preds-concat-eap100_ep005_penalty15-chr_1_5.txt` and\n `distances_concat-chr_1_5.txt`: Five macrochromosomes concatenated, beta =\n 100 (the main paper analysis). *\n `preds-concat-eap100_ep005_penalty15-chr_1_5_Z.txt` and\n `distances_concat-chr_1_5_Z.txt`: Five macrochromosomes + Z chromosome,\n beta = 100. * `preds-merged-eap100_ep005_penalty15.txt` and\n `distances_concat_merged.txt`: Merged analysis. *\n `eap40_ep005_noprior-concat_1_5/` and `eap40_ep005_noprior-concat_1_5_Z/`:\n Concatenated analyses without prior. ## ARGs from simulation experiments\n (`avian-simulations-tree_sequences.tar.gz`) Tree sequence objects were\n simulated using `msprime`. We used Stiller et al. 2024 avian phylogeny as\n our demographic model. * `default-{SIZE}-{INDEX}.ts`: Only Neoaves, size\n is 250Kb. * `favian-{SIZE}-{INDEX}.ts`: Full Aves species tree, size is\n set to 500Kb. * `anomaly_case-{BRANCH}_{CHANGE}-{SIZE}-{INDEX}.ts`: ARGs\n deviating from the background (`default-*` or `favian-*`) parameterization\n (such as population size change, recombination rate change, and\n admixture). `{SIZE}` correspond to independent block lengths that we used\n to simulate ARGs independently. Resulting sequences were concatenated\n based on the `{INDEX}` value. You can use the below Python snippet to read\n the tree sequence objects. ``` import tskit # Load the tree sequence\n object ts = tskit.load(\"path/to/your/file.ts\") # Print summary\n information print(ts) ``` ## Code/software Phlag is available on\n [GitHub](https://github.com/bo1929/phlag), and the version used in the\n paper can be found on [Zenodo](https://doi.org/10.5281/zenodo.20244401)."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Institute of General Medical Sciences","funderIdentifier":"https://ror.org/04q48ey07","awardTitle":"\n        Biology-aware machine learning methods for characterizing microbiome\n        genotype and phenotype\n      ","awardNumber":"1R35GM142725-01","awardUri":"https://reporter.nih.gov/project-details/10275055"},{"funderIdentifierType":"ROR","funderName":"San Diego Supercomputer 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Previous research reported that the maximum forest canopy height (Hmax) at global and regional scales could be explained by variations in water or energy availability, that is, the water- or energy-related hypothesis. However, fundamental gaps remain in our understanding of how different drivers (i.e., water and energy) contribute to the Hmax at the local scale. In this study, we selected eight dynamic forest plots (20–30 ha) across a latitudinal gradient (from 21.6° N to 48.1° N) in China and measured the canopy structure using airborne light detection and ranging (LiDAR) data. Based on the LiDAR point cloud data, we extracted the maximum tree height (Hmax) in a 20 × 20 m quadrat as a proxy for canopy height, and the topographic wetness index (TWI) and digital terrain model-derived insolation (DTMI) were calculated as proxies for water and energy conditions. We used a linear mixed model and spatial simultaneous autoregressive error model to quantify how TWI and DTMI contributed to variations in Hmax at the local scale. We found that the positive effect of TWI was stronger in subtropical and tropical forests, highlighting that water was the main factor that drives the canopy height pattern in these regions. In contrast, although the effects of DTMI can be both positive and negative, its relative contribution was higher in temperate forest plots than in other forest types, supporting the idea that energy input is more critical for Hmax in temperate forests. Overall, our study revealed the directional change from energy to water limitation from temperate to subtropical and tropical forests. 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