cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7483/opendata.cms.heqp.3v9d
title: /RSGravitonToQuarkQuark_kMpl01_M_750_TuneCUETP8M1_13TeV_pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hesv.kmh2
title: /HeavyCompositeMajoranaNeutrino_L13000_M3500_mumujj_CalcHep/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hfe4.5ka5
title: /BlackHole_BH2_MD-3000_MBH-4000_n-6_TuneCUETP8M1_13TeV-blackmax/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hfgt.ln3f
title: /GluGluToRadionToHHTo2B2Tau_M-400_narrow_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hfj0.gxrp
title: /DMV_NNPDF30_Axial_Mphi-1500_Mchi-150_gSM-0p25_gDM-1p0_v2_13TeV-powheg/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hfty.rk5a
title: /GluGluHToZG_M-120_13TeV_powheg_pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12_ext1-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hg78.tlcb
title: /SLQ_Rhanded-MLQ1000g1r0p2_13TeV-calchep/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hgcd.vds2
title: /ZprimeToZhToZinvhZZTo4l_2HDM_M-800_13TeV-madgraph/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.hgnw.cjd7
title: /BBbarDMJets_pseudoscalar_Mchi-10_Mphi-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hh38.mofr
title: /DisplacedSUSY_StopToBL_M-200_CTau-100_TuneCUETP8M1_13TeV_pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hhjn.jjwb
title: /DisplacedSUSY_StopToBL_M-1000_CTau-1_TuneCUETP8M1_13TeV_pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hhle.7gyi
title: /Taustar_TauG_L10000_m2500_13TeV-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hhoe.fqb7
title: /DMV_NNPDF30_Axial_Mphi-1000_Mchi-400_gSM-0p25_gDM-1p0_v2_13TeV-powheg/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hhpe.1smd
title: /BprimeTToZB_M-1100_RH_TuneCUETP8M1_13TeV-madgraph-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hi4q.dxhv
title: /gluinoGMSB_M1400_ctau1000p0_TuneCUETP8M1_13TeV_pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hidn.fw47
title: /QstarToQW_M-4500_TuneCUETP8M1_13TeV-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hj0x.9a71
title: /RPVresonantToETau_M-5000_LLE_LQD-05_TuneCUETP8M1_13TeV-calchep-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hj22.ck6w
title: /BlackHole_BH1_MD-3000_MBH-6000_n-2_TuneCUETP8M1_13TeV-blackmax/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.hj3h.youe
title: /GluGluSpin0ToZG_ZToLL_W-1p4_M-770_TuneCUEP8M1_13TeV-pythia8/RunIIFall15MiniAODv2-PU25nsData2015v1_76X_mcRun2_asymptotic_v12-v1/MINIAODSIM
DOI: 10.6084/m9.figshare.20388258.v2
title: Plant water source_SWB partitioning_raw_data

description: Soil water contents and isotope data were used in the article entitled " <strong>Partitioned Soil Water Balance and Its Link with Water Uptake Strategy under Apple Trees in the Loess-covered Region</strong> ", which is published in Water Resources Research.
DOI: 10.6084/m9.figshare.15074301
title: The genotyping data from BnaPan50K capture panel

description: The DH population from <i>B. napus</i> was genotyping by BnaPan50K capture array.
DOI: 10.6084/m9.figshare.15074301.v3
title: The genotyping data from BnaPan50K capture panel

description: The DH population from <i>B. napus</i> was genotyping by BnaPan50K capture array.
DOI: 10.6084/m9.figshare.15074301.v2
title: The genotyping data from BnaPan50K capture array

description: The DH population from <i>B. napus</i> was genotyping by BnaPan50K capture array.
DOI: 10.15121/1838419
title: WHOLESCALE Catalog of Rock Samples at San Emidio Nevada collected in January 2021

description: This submission contains information on thirty-six rock samples collected from San Emidio, Nevada during January, 2021 for Subtask 2.3 of the WHOLESCALE project. The following resources include a .zip of rock sample photos taken in the field, a .zip of rock sample photos taken in the laboratory at UW-Madison, and an excel catalog of rock samples with information on sample name, rock type, coordinates of sample location, structural measurements, field notes, observations for plug preparation (e.g., weathering, ability to be cut and cored), and rock descriptions. It should be noted that not every sample was photographed in the field. Names and descriptions of rock formation units are taken from Rhodes et al. (2011). The README.txt file is a description of this submission.
DOI: 10.5519/qd.lviniuhu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.15784/601592
title: Preliminary nitrous oxide site preference isotopic data for last deglaciation from Taylor Glacier

description: Measurements of the isotopic composition of atmospheric nitrous oxide from samples from the Taylor Glacier, Antarctica, spanning the last deglaciation (21-11 ka) and part of the last glacial period (40 to 36 ka). Data set includes the site preference of 15-N in N2O. A manuscript describing these data is currently in preparation. Data are referenced to in house air standards at OSU which are currently being cross calibrated with other laboratories.
DOI: 10.5281/zenodo.6592097
title: Acknowledged scholars extracted from open access journals

description: <strong>Published paper</strong><br> For details of the data development: Kusumegi, K., Sano, Y. Dataset of identified scholars mentioned in acknowledgement statements. Sci Data 9, 461 (2022). https://doi.org/10.1038/s41597-022-01585-y To use the data, please cite the above mentioned paper . <strong>Data on scholars mentioned in acknowledgments extracted from open access journals.</strong> This data firstly updated the repository at University of Tsukuba (Division of Policy and Planning Sciences Commons). https://commons.sk.tsukuba.ac.jp/data_en <pre> <strong>Each csv correspond to:</strong> biology.csv as PLOS Biology compbiology.csv as PLOS Computational Biology genetics.csv as PLOS Genetics medicine.csv as PLOS Medicine ntds.csv as PLOS Neglected Tropical Diseases pathogenes.csv as PLOS Pathogens plosone.csv as PLOS ONE srep.csv as Scientific Reports Data details: Doi: DOI of the paper PaperId: Paper ID in MAG AcknowledgedId: Acknowledged scholar's ID in MAG CollaborationApproach: Acknowledged scholar is identified by collaboration relationship CitationApproach: Acknowledged scholar is identified by citation relationship </pre>
DOI: 10.15121/1879102
title: WHOLESCALE Catalog of Rock Samples at San Emidio Nevada collected in January 2021 Version 2.0

description: This submission includes an update to the WHOLESCALESamples2021January.xls file where the strikes and dips initially reported have been corrected to comply with the right hand rule. The updated excel file and a link to the original submission are included in this report. The original submission "WHOLESCALE Catalog of Rock Samples at San Emidio Nevada collected in January 2021" contains information on thirty-six rock samples collected from San Emidio, Nevada during January, 2021 for Subtask 2.3 of the WHOLESCALE project. The following resources include a .zip of rock sample photos taken in the field, a .zip of rock sample photos taken in the laboratory at UW-Madison, and an excel catalog of rock samples with information on sample name, rock type, coordinates of sample location, structural measurements, field notes, observations for plug preparation (e.g., weathering, ability to be cut and cored), and rock descriptions. It should be noted that not every sample was photographed in the field. Names and descriptions of rock formation units are taken from Rhodes et al. (2011). The README.txt file is a description of this submission.
DOI: 10.6084/m9.figshare.19991204
title: Social effects of rabies infection in male vampire bats (Desmodus rotundus)

description: Dataset and R code for manuscript: Social effects of rabies infection in male vampire bats (Desmodus rotundus).
DOI: 10.6084/m9.figshare.19991204.v4
title: Social effects of rabies infection in male vampire bats (Desmodus rotundus)

description: Dataset and R code for manuscript: Social effects of rabies infection in male vampire bats (Desmodus rotundus).
DOI: 10.6084/m9.figshare.19991204.v3
title: Social effects of rabies infection in male vampire bats (Desmodus rotundus)

description: Dataset and R code for manuscript: Social effects of rabies infection in male vampire bats (Desmodus rotundus).
DOI: 10.6084/m9.figshare.20416374
title: Social Economic Determinants of Bitcoin Price

description: This dataset contains Bitcoin price and social economic factors that influence Bitcoin price.
DOI: 10.5281/zenodo.6950787
title: Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 10/30/2016

description: Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5-km intervals and from 23.7 UT 10/29/2016 to 8.9 UT 10/30/2016 at 0.1-hour intervals. Errors of these values are also included. -999 represents missing values.
DOI: 10.5517/ccdc.csd.cc29d6yw
title: CCDC 2126908: Experimental Crystal Structure Determination

description: Related Article: Y. N. Sambasiva Rao, Palash Ghosh, Prathama S. Mainkar, Srivari Chandrasekhar|2022|Org.Lett.|24|5372|doi:10.1021/acs.orglett.2c02046
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.6950240
title: Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?

description: Input files and data used in the paper "Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?". MESA models were computed with version 12115 of MESA. COSMIC models new computed with version 3.4. README file included.
DOI: 10.34740/kaggle/dsv/4018089
title: Russian Invasion of Ukraine
DOI: 10.6084/m9.figshare.20416452.v1
title: Additional file 1 of Validation of the Dutch translation of the quality of recovery-15 scale

description: Additional file 1.
DOI: 10.6084/m9.figshare.20416452
title: Additional file 1 of Validation of the Dutch translation of the quality of recovery-15 scale

description: Additional file 1.
DOI: 10.6084/m9.figshare.20416500
title: Additional file 1 of Developing key performance indicators for the Canadian chiropractic profession: a modified Delphi study

description: Additional file 1: Expert’s evaluation of the indicators during the first and second rounds.
DOI: 10.6084/m9.figshare.20416500.v1
title: Additional file 1 of Developing key performance indicators for the Canadian chiropractic profession: a modified Delphi study

description: Additional file 1: Expert’s evaluation of the indicators during the first and second rounds.
DOI: 10.6084/m9.figshare.20416533
title: Additional file 1 of Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease

description: Additional file 1. Supplementary Table S1: The p-value significance for imaging-genetics analysis. Sub-tables (a), (b), (c) show the p-value significance of imaging-geneticsanalysis for all SNP-ROI pairs on three data sets (i.e., CN vs AD, CN vsLMCI, and CN vs EMCI), respectively. For each ROI-SNP pair, the p-value is shown.
DOI: 10.6084/m9.figshare.20416533.v1
title: Additional file 1 of Identifying genetic markers enriched by brain imaging endophenotypes in Alzheimer’s disease

description: Additional file 1. Supplementary Table S1: The p-value significance for imaging-genetics analysis. Sub-tables (a), (b), (c) show the p-value significance of imaging-geneticsanalysis for all SNP-ROI pairs on three data sets (i.e., CN vs AD, CN vsLMCI, and CN vs EMCI), respectively. For each ROI-SNP pair, the p-value is shown.
DOI: 10.6084/m9.figshare.20416362.v1
title: The population genotype data generated from BnaPan50T SNP panel

description: A genotype dataset generated from 90 targeted sequenced DH lines of rapeseed.
DOI: 10.6084/m9.figshare.20416362
title: The population genotype data generated from BnaPan50T SNP panel

description: A genotype dataset generated from 90 targeted sequenced DH lines of rapeseed.
DOI: 10.6084/m9.figshare.20416581
title: Factors associated with the very high caesarean section rate in urban areas of Vietnam

description: delcae: mode of delivery (1=caesarean section, 0=vaginal birth) motage: mother's age (year) moteducodthree: mother's education level (Primary & Secondary =2, High school/ Intermediate degree=3, University/ Postgraduate=4) faminc: family income motaddoneurb: place of residence (1=urban, 0=rural) motchilcodtwo: parity prepro: prenancy problem bmi: pre-pregnancy body max index weight gain: gestational weight gain delpripub: place of birth (1=private, 0=public) babsex: baby's sex (1=male, 0=female) babweibir: baby's birth weight
DOI: 10.6084/m9.figshare.20416599
title: Socio-Economic Development of Asian Russia - datasets for Khabarovsk and Primorsky Krais

description: The datasets represent topographic description (cost and accessibility maps) of Khabarovsk and Primorsky Krais of the Russian Far East divided into unit areas with a 10x10 km grid in WGS84. The datasets are in MID/MIF formats to be processed in QGIS with use of self-written open source software. The datasets are used to model single or multiple socio-economic scenarios of regional spatial development and inter-regional economic cooperation.
DOI: 10.5281/zenodo.3987714
title: Annual rain erosion (R) in Brazil

description: The erosivity data in Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF</strong> <strong>format </strong>and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>. Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487. The article was submitted for publication. Dados_Erosividade_BR.csv - Data used to model the models.<br> eros_cubist.tif - Erosivity image generated by the cubist model<br> eros_gbm.tif - Image of erosivity generated by the gbm model<br> eros_lm.tif - Erosivity image generated by the linear model<br> eros_rf.tif - Erosivity image generated by the random forest model
description: We are grateful for the financial support (doctoral scholarship) granted by the Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) under process number: 88882.450196 / 2019-01 and Financial Code 001. We are also grateful to the Graduate Program in Agronomy of the UEPG, to the Group of Studies and Research in Water and Soil Management (GEMAS) of the State University of Ponta Grossa-PR (UEPG).
DOI: 10.5281/zenodo.3987713
title: Annual rain erosion (R) in Brazil

description: The erosivity data in Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF</strong> <strong>format </strong>and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>. Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487. The article was submitted for publication. Dados_Erosividade_BR.csv - Data used to model the models.<br> eros_cubist.tif - Erosivity image generated by the cubist model<br> eros_gbm.tif - Image of erosivity generated by the gbm model<br> eros_lm.tif - Erosivity image generated by the linear model<br> eros_rf.tif - Erosivity image generated by the random forest model
description: We are grateful for the financial support (doctoral scholarship) granted by the Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) under process number: 88882.450196 / 2019-01 and Financial Code 001. We are also grateful to the Graduate Program in Agronomy of the UEPG, to the Group of Studies and Research in Water and Soil Management (GEMAS) of the State University of Ponta Grossa-PR (UEPG).
DOI: 10.34740/kaggle/dsv/4018381
title: WallStreetBets 2022
DOI: 10.26186/147053
title: Flinders Commonwealth Marine Reserve (GA0337)

description: This resource contains multibeam bathymetry data for the continental shelf area of Flinders Commonwealth Marine Reserve, northeast Tasmania. Multibeam data were collected by Geoscience Australia and University of Tasmania during the period of 24 June - 07 July 2012 onboard the RV Challenger using a Kongsberg EM3002 multibeam sonar. The Flinders Marine Reserve survey was led by Dr. Scott Nichol (Geoscience Australia). The survey was conducted to broadly classify the seabed into hard (bedrock reef), soft (sedimentary) and mixed substrate types at select locations across the shelf. The dataset contains a 2m-resolution, 32-bit floating point GeoTIFF file of the bathymetry in the study area derived from the processed EM3002 bathymetry data, using Caris HIPS and SIPS software. Attribution statement: Data was sourced from the NERP Marine Biodiversity Hub. The Marine Biodiversity Hub is supported through funding from the Australian Government's National Environmental Research Program (NERP), administered by the Department of Sustainability, Environment, Water, Population and Communities (DSEWPAC). This dataset is not to be used for navigational purposes. This dataset is published with the permission of the CEO, Geoscience Australia.
DOI: 10.6084/m9.figshare.20378268.v2
title: Data anemia Rautahat v3wra.xlsx

description: For the study, a hospital-based cross-sectional study was conducted among women of the reproductive age group (15-45) from 15th March to 15th May 2022 after obtaining ethical clearance from the Nepal health research council. 375 women participants were selected via a simple random sampling technique. Participants underwent an interview after informed written consent followed by blood sample collection. Through a semi-structured questionnaire, the data was obtained. Then data was entered and analyzed via Microsoft Excel 2019 and Statistical Package for Social Sciences version 22.0.
DOI: 10.5281/zenodo.6951043
title: Effects of topicality in the Interpretation of implicit consequentiality: Evidence from offline and online referential processing in Korean

description: This supplementary material contains data files and R scripts that accompany the article to be published in Linguistics (volume 61, 2023).
DOI: 10.5281/zenodo.6951042
title: Effects of topicality in the Interpretation of implicit consequentiality: Evidence from offline and online referential processing in Korean

description: This supplementary material contains data files and R scripts that accompany the article to be published in Linguistics (volume 61, 2023).
DOI: 10.20372/aau_rdr/chgwjp/jivvgj
title: Raw data .xlsx

description: :unav
DOI: 10.5281/zenodo.6951102
title: Submitted Publication

description: This is the data artifact for a submitted publication to Supercomputing 2022. What to open<br> ============ Start in `01_scripts-measurements.zip` with its central README file.<br> This file summarizes all masurements & setups. Source code referenced from this README file is either open source on GitHub in WarpX and AMReX or patched.<br> Patches are stored in `02_sources_...`. The directory `03_larger_data_...` contains larger data, such as science runs (source data for figures) and profiles for flop measurements on GPU.
DOI: 10.5281/zenodo.6464240
title: Submitted Publication

description: This is the data artifact for a submitted publication to Supercomputing 2022. What to open<br> ============ Start in `01_scripts-measurements.zip` with its central README file.<br> This file summarizes all masurements & setups. Source code referenced from this README file is either open source on GitHub in WarpX and AMReX or patched.<br> Patches are stored in `02_sources_...`. The directory `03_larger_data_...` contains larger data, such as science runs (source data for figures) and profiles for flop measurements on GPU.
DOI: 10.6084/m9.figshare.20417595.v1
title: Untitled Item

description: MAGs of AOA and NOB from hadal trench sediment samples
DOI: 10.6084/m9.figshare.20417595
title: MAGs

description: MAGs of AOA and NOB from hadal trench sediment samples
DOI: 10.6084/m9.figshare.20417595.v2
title: MAGs

description: MAGs of AOA and NOB from hadal trench sediment samples