Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
40
datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “Deglaciation”
Northeast Pacific deoxygenation and volcanism during the last deglaciation
<p><strong>File structure:</strong></p> <p><strong>Source data</strong></p> <ul> <li>Contains source data to main text and extended data figures. Data sources are identified within and listed below.</li> </ul> <p> </p> <p><strong>Computer codes</strong></p> <ul> <li><strong>Geochemical inversion</strong> –</li> </ul> <ul> <li>“Data for geochemical inversion.xlsx”: contains Gulf of Alaska sediment and volcanic/terrigenous endmember geochemical data used for data inversion.</li> <li>“geochemical inversion.r”: R script used to perform geochemical inversion.</li> <li>“GOA inversion fraction.csv”: Result of the geochemical inversion, including the volcanic and terrigenous fractions in each Gulf of Alaska sediment sample.</li> <li>“GOA inversion residual.csv”: Result of the geochemical inversion, including the residuals of each element.</li> <li><strong>cluster volcanic geochemical data </strong>– <ul> <li> “Database of volcanic geochemistry.xlsx”: compiled database of the geochemistry of volcanic endmember samples.</li> <li> “cluster.r”: R script used to perform cluster analysis on the volcanic samples.</li> <li> “Clustered volcanic data.csv”: the results of cluster analysis.</li> <li> “Dendroplot.r”: R script used to plot the dendrogram for the cluster analysis.</li> <li> “dendro 15 complete euclidean.pdf”: The dendrogram.</li> <li> “Volcanic endmembers.csv”: final geochemical volcanic endmembers based on the cluster analysis.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Global volcanic eruption compilation –</strong></li> </ul> <ul> <li>“eruption.database.intcal20.xlsx”: This file includes the eruption database compiled by this study, as well as the previous compilation of Huybers and Langmuir 2009 EPSL (referred to as HL09).</li> <li>“eruption.ratio.R” and “volc.freq.R”: These are R scripts that compute the eruption frequency of glaciated and unglaciated volcanoes using the eruption database. “eruption.ratio.R”calls the function inside “volc.freq.R”.</li> <li>“Volcanic eruption summary.xlsx”: This file contains the outputs of the R scripts.</li> </ul> <p> </p> <ul> <li><strong>PISM sensitivity experiment – </strong></li> </ul> <ul> <li>“ciscyc.5km.epica.ts.10a.nc” and other netcdf files: The output of PISM sensitivity experiments, from <em>Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3606536">https://doi.org/10.5281/zenodo.3606536</a></em>. Click the link to see the documentation of these files.</li> <li>“temperature timeseries.xlsx”: the temperature forcing used in the sensitivity experiments.</li> <li>“PISM sensitivity.r”: R script used to analyse the PISM sensitivity experiments, including data binning, lag correlation and regression between ice sheeting response and temperature forcing.</li> <li>“PISM sensitivity.xlsx”: Output of the R script.</li> <li>“GOA.calibration.csv”: SST record from the Gulf of Alaska site 85JC/U1419, calibrated using bayspline.</li> <li>“GOA.Ensemble.csv”: 1000 ensemble output of the bayspline calibration.</li> <li>“predict ice volume SST.r”: R script used to predict the response of CIS ice volume to GOA SST forcing. The script will call the results of PISM sensitivity experiments in “PISM sensitivity.xlsx” and the GOA SST forcing in “GOA.calibration.csv” and “GOA.calibration.csv”.</li> <li>“PISM ice vol GOA SST.csv”: predicted PISM ice vol based on GOA SST forcing and taking into account all sensitivity experiments and the uncertainty in SST reconstruction.</li> <li>“PISM ice vol GOA SST model.csv”: predicted PISM ice vol based on GOA SST forcing based on each sensitivity experiment and the uncertainty in SST reconstruction.</li> </ul> <p> </p> <p><strong>Please cite the following studies when using the data, in addition to citing the present study:</strong></p> <p><strong>GOA age model, IRD and MAR:</strong></p> <p>Walczak, M. H. et al. Phasing of millennial-scale climate variability in the Pacific and Atlantic Oceans. Science 370, 716–720 (2020).</p> <p>Velle, J. H. et al. High resolution inclination records from the Gulf of Alaska, IODP Expedition 341 Sites U1418 and U1419. Geophys. J. Int. 229, 345–358 (2022).</p> <p>Heaton, T. J. et al. Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP). Radiocarbon 62, 779–820 (2020).</p> <p><strong>GOA SST: </strong></p> <p>Praetorius, S. K. et al. North Pacific deglacial hypoxic events linked to abrupt ocean warming. Nature 527, 362–366 (2015).</p> <p>Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022).</p> <p>Tierney, J. E. & Tingley, M. P. BAYSPLINE: A New Calibration for the Alkenone Paleothermometer. Paleoceanogr. Paleoclimatology 33, 281–301 (2018).</p> <p><strong>GOA benthic foraminifera assemblage:</strong></p> <p>Belanger, C. L., Sharon, Du, J., Payne, C. R. & Mix, A. C. North Pacific deep-sea ecosystem responses reflect post-glacial switch to pulsed export productivity, deoxygenation, and destratification. Deep Sea Res. Part Oceanogr. Res. Pap. 164, 103341 (2020).</p> <p>Sharon, Belanger, C., Du, J. & Mix, A. Reconstructing Paleo-oxygenation for the Last 54,000 Years in the Gulf of Alaska Using Cross-validated Benthic Foraminiferal and Geochemical Records. Paleoceanogr. Paleoclimatology 36, e2020PA003986 (2021).</p> <p><strong>GOA productivity:</strong></p> <p>Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022).</p> <p>Addison, J. A. et al. Productivity and sedimentary δ15N variability for the last 17,000 years along the northern Gulf of Alaska continental slope. Paleoceanography 27, PA1206 (2012).</p> <p><strong>GOA bulk sediment neodymium isotopes:</strong></p> <p>Du, J., Haley, B. A., Mix, A. C., Walczak, M. H. & Praetorius, S. K. Flushing of the deep Pacific Ocean and the deglacial rise of atmospheric CO 2 concentrations. Nat. Geosci. 11, 749–755 (2018).</p> <p><strong>GOA volcanic endmember data compilation:</strong></p> <p>Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120.</p> <p>Sarbas, B., Jochum, K. P., Nohl, U. & Hofmann, A. W. GEOROC, the MPI geochemical rock database: a new tool for geochemists. Eos Trans. AGU 80, F1184 (1999).</p> <p><strong>Global and regional volcanic eruption data compilation:</strong></p> <p>Huybers, P. & Langmuir, C. Feedback between deglaciation, volcanism, and atmospheric CO2. Earth Planet. Sci. Lett. 286, 479–491 (2009).</p> <p>Global Volcanism Program, 2013. Volcanoes of the World, v. 4.8.7. 10.5479/si.GVP.VOTW4-2013. (2013).</p> <p>Bryson, R. U., Bryson, R. A. & Ruter, A. A calibrated radiocarbon database of late Quaternary volcanic eruptions. EEarth Discuss 1, 123–134 (2006).</p> <p>Watt, S. F. L., Pyle, D. M. & Mather, T. A. The volcanic response to deglaciation: Evidence from glaciated arcs and a reassessment of global eruption records. Earth-Sci. Rev. 122, 77–102 (2013).</p> <p>Crosweller, H. S. et al. Global database on large magnitude explosive volcanic eruptions (LaMEVE). J. Appl. Volcanol. 1, 4 (2012).</p> <p>Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120.</p> <p>Praetorius, S. et al. Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation. Earth Planet. Sci. Lett. 452, 79–89 (2016).</p> <p>Wilcox, P. S. et al. A new set of basaltic tephras from Southeast Alaska represent key stratigraphic markers for the late Pleistocene. Quat. Res. 92, 246–256 (2019).</p> <p>Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016).</p> <p><strong>GIA models:</strong></p> <p>Roy, K. & Peltier, W. R. Relative sea level in the Western Mediterranean basin: A regional test of the ICE-7G_NA (VM7) model and a constraint on late Holocene Antarctic deglaciation. Quat. Sci. Rev. 183, 76–87 (2018).</p> <p>Lambeck, K., Purcell, A. & Zhao, S. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quat. Sci. Rev. 158, 172–210 (2017).</p> <p><strong>PISM sensitivity experiments and temperature forcing:</strong></p> <p>Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. & Kleman, J. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle. The Cryosphere 10, 639–664 (2016).</p> <p>Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536</p> <p>Dansgaard, W. et al. Evidence for general instability of past climate from a 250-kyr ice-core record. Nature 364, 218–220 (1993).</p> <p>Andersen, K. K. et al. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004).</p> <p>Jouzel, J. et al. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science 317, 793–796 (2007).</p> <p>Petit, J. R. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999).</p> <p>Herbert, T. D. et al. Collapse of the California Current During Glacial Maxima Linked to Climate Change on Land. Science 293, 71–76 (2001).</p> <p><strong>Be10 data compilation:</strong></p> <p>Lesnek, A. J., Briner, J. P., Baichtal, J. F. & Lyles, A. S. New constraints on the last deglaciation of the Cordilleran Ice Sheet in coastal Southeast Alaska. Quat. Res. 96, 140–160 (2020).</p> <p>Haeussler, P. J. et al. Late Quaternary deglaciation of Prince William Sound, Alaska. Quat. Res. 1–20 (2021) doi:10.1017/qua.2021.33.</p> <p>Walcott, C. K., Briner, J. P., Baichtal, J. F., Lesnek, A. J. & Licciardi, J. M. Cosmogenic ages indicate no MIS 2 refugia in the Alexander Archipelago, Alaska. Geochronology 4, 191–211 (2022).</p> <p>Briner, J. P. et al. The last deglaciation of Alaska. Cuad. Investig. Geográfica 43, 429–448 (2017).</p> <p>Tulenko, J. P., Briner, J. P., Young, N. E. & Schaefer, J. M. Beryllium-10 chronology of early and late Wisconsinan moraines in the Revelation Mountains, Alaska: Insights into the forcing of Wisconsinan glaciation in Beringia. Quat. Sci. Rev. 197, 129–141 (2018).</p> <p>Menounos, B. et al. Cordilleran Ice Sheet mass loss preceded climate reversals near the Pleistocene Termination. Science 358, 781–784 (2017).</p> <p>Dulfer, H. E., Margold, M., Engel, Z., Braucher, R. & Team, A. Using 10Be dating to determine when the Cordilleran Ice Sheet stopped flowing over the Canadian Rocky Mountains. Quat. Res. 102, 222–233 (2021).</p> <p>Lesnek, A. J., Briner, J. P., Lindqvist, C., Baichtal, J. F. & Heaton, T. H. Deglaciation of the Pacific coastal corridor directly preceded the human colonization of the Americas. Sci. Adv. 4, eaar5040 (2018).</p> <p>Tulenko, J. P., Briner, J. P., Young, N. E. & Schaefer, J. M. The last deglaciation of Alaska and a new benchmark 10Be moraine chronology from the western Alaska Range. Quat. Sci. Rev. 287, 107549 (2022).</p>
Modal shift in North Atlantic seasonality during the last deglaciation
<p>Raw data for Brummer et al. (2020) published in Climate of the Past (https://doi.org/10.5194/cp-16-1-2020)</p>
A global synthesis of high-resolution stable isotope data from benthic foraminifera of the last deglaciation
<p>In paleoceanography, carbon and oxygen stable isotope ratios from benthic foraminifera are used as tracers of physical and biogeochemical properties of the deep ocean. We present the first version of the Ocean Carbon Cycling working group database, of stable isotope ratios of oxygen and carbon from benthic foraminifera from deep ocean sediment cores from the Last Glacial Maximum (LGM, 23-20 ky before present (BP)) to the Holocene (<10 ky BP) with a particular focus on the early last deglaciation (20-15 ky BP). It includes 287 globally distributed coring sites, with metadata, isotopic and chronostratigraphic information, and age models. A quality check was performed for all data and age models. Sites with at least millennial resolution were preferred, because the main goal is to resolve ocean changes associated with the last deglaciation on at least millennial timescales. Software tools were produced to access and analyze the data, and are included with this publication. Deep water mass structure as well as differences between the early deglaciation and LGM are captured by the data in the compilation, even though its coverage is still sparse in many ocean regions. We find high correlations among time series calculated with different age models at sites that allow such analysis. The database provides a useful dynamical approach to map physical and biogeochemical changes of the ocean throughout the last deglaciation.</p>
The Meltwater Pulse1A Triggered an Extreme Cooling Event: Evidence From Southern China. Meltwater Pulse Cooling Event (MCE). Winter temperature data during the last deglacial of Huguangyan Maar lake, Surface water temperature and seasonal diatom assemblage data of Huguangyan and Yunlong Lake.
<p>Here we present results of The lake averaged monthly mean surface water temperature over the period from September 2013 to August 2015 from Yunlong Tianchi Lake(YL)(25°52.2′N, 99°16.8′E, altitude: 2551 m a.s.l), southwestern China. The dataset include sediment trap main diatom percentages over the period from September 2013 to August 2015 from YL. Lake water temperature profiles at different depths (1, 3, 6, 9, 11, 13, 16 m) from November 2008 to May 2009 in Huguang Maar Lake (HML)(21°9′N, 110°17′E), Southern China. AMS radiocarbon dates of plant remains and bulk sediment samples for Huguangyan Maar Lake over the last ~17 cal ka BP. The main diatom assemblage percentages (%) from 17 to 10 cal ka BP at Huguangyan Maar Lake. Diatom-based reconstruction of winter temperature (WT) from 17 to 10 cal ka BP at Huguangyan Maar Lake.</p>
500-year periodic vegetation and monsoonal climate oscillations during the last deglaciation in East Asia
<p>1 Core Xiaolongwan description</p> <p>2 Age-depth models</p> <p>3 Pollen counts</p> <p>4 Pollen concentration</p> <p>5 Pollen percentage</p> <p>6 HHT Betula</p> <p>7 HHT Boreal Conifer with Herb</p> <p>8 HHT Artemisia</p> <p>9 HHT Broadleaved Tree</p> <p>10 Long chain <em>n</em>-alkanes δ<sup>13</sup>C</p>
How Should Snowball Earth Deglaciation Start
<p>CAM3&CLM3 simulation results that support the findings of this study are archived here.</p> <p># Published version </p>
New data for the manuscript "Deglacial ventilation changes in the deep Southwest Pacific" submitted to Paleoceanography and Paleoclimatology
<p>This repository contains new data reported in the "Deglacial ventilation changes in the deep Southwest Pacific" submitted to Paleoceanography and Paleoclimatology by Dai et al.</p> <p>File <a href="https://zenodo.org/api/files/8e3be8c4-58af-41cd-8ee1-9e3e2b6c5ae5/MD97-2106%20benthic%20radiocarbon.txt">MD97-2106 benthic radiocarbon.txt</a> contains all benthic radiocarbon data in this manuscript.</p> <p>File <a href="https://zenodo.org/api/files/8e3be8c4-58af-41cd-8ee1-9e3e2b6c5ae5/MD97-2106%20G%20bulloides%20trace%20elements.txt">MD97-2106 G bulloides trace elements.txt</a> contains trace-element-to-calcium ratios (Mg/Ca, Al/Ca, Mn/Ca) in this manuscript.</p> <p> </p>
Caught in a bottleneck: habitat loss for woolly mammoths in central North America and the ice-free corridor during the last deglaciation
<p>The dataset is to describe the habitat structure and bioenergetic characteristics of woolly mammoths (<i><span>Mammuthus primigenius</span></i>) in North America during the last deglaciation between 15 and 10 ka. The habitat structure includes fractional woody cover (FWC) and net primary productivity (NPP) for 20 plant functional types (PFTs). NPP is based on the dynamic vegetation model LPJ-GUESS (LPJG). FWC is based on LPJ-GUESS and fossil pollen records in the Neotoma Paleoecology Database. The bioenergetic characteristics of woolly mammoths are the results of Niche Mapper, including metabolic rate, forage consumption, freshwater consumption, and other bioenergetic traits of individual woolly mammoths. These data were conducted at a temporal resolution of 100-year means of climate simulations every 1,000 years, from 15 to 10 ka. Simulations were run for all of North America except Alaska (10° – 80°N, 140° – 45°W) at a spatial resolution of 0.5°x0.5° grid latitude and longitude.</p>
Holocene deglaciation drove rapid genetic diversification of Atlantic walrus
<p>Rapid global warming is severely impacting Arctic ecosystems and is predicted to transform the abundance, distribution, and genetic diversity of Arctic species, though these linkages are poorly understood. We address this gap in knowledge using palaeogenomics to examine how earlier periods of global warming influenced the genetic diversity of Atlantic walrus (<em>Odobenus rosmarus rosmarus</em>), a species closely associated with sea ice and shallow-water habitats. We analysed 82 ancient and historical Atlantic walrus mitochondrial genomes (mitogenomes), including now-extinct populations in Iceland and the Canadian Maritimes, to reconstruct the Atlantic walrus' response to Arctic deglaciation. Our results demonstrate that the phylogeography and genetic diversity of Atlantic walrus populations were initially shaped by the Last Glacial Maximum (LGM), surviving in distinct glacial refugia, and subsequently expanding rapidly in multiple migration waves during the late Pleistocene and early Holocene. The timing of diversification and establishment of distinct populations corresponds closely with the chronology of the glacial retreat, pointing to a strong link between walrus phylogeography and sea ice. Our results indicate that accelerated ice loss in the modern Arctic may trigger further dispersal events, likely increasing the connectivity of northern stocks while isolating more southerly stocks putatively caught in small pockets of suitable habitat. </p>
Marinoan Snowball Earth: The Impact of Deglaciation Duration on the Sea-Level History of Continental Margins
<p>Paleogeographies, ice histories, and predicted relative sea level output are available here as Matlab (.mat) files. Complementary text (.txt) files are included for latitude, longitude, and time. For different file formats or any questions to their use, please contact the corresponding author, Freya K Morris at: morrisfreya15@outlook.com</p>
New data for Dai, Y., Yu, J., Ren, H. et al. Deglacial Subantarctic CO2 outgassing driven by a weakened solubility pump. Nat Commun 13, 5193 (2022)
<p>This repository contains new data reported in the article entitled "Deglacial Subantarctic CO<sub>2</sub> outgassing driven by a weakened solubility pump" in Nature Communications by Dai et al.</p> <p>File d11B MgCa.txt contains boron isotope and Mg/Ca data in planktic foraminifera <em>G. bulloides</em> at site MD97-2106.</p> <p>File d15N.txt contains nitrogen isotope data in planktic foraminifera <em>G. bulloides</em> at site MD97-2106.</p>
Beryllium Isotopes in Maar Lake Sediments Response to Rapid Climate Change Since the Last Deglaciation
<p>Data for "<span>Beryllium Isotopes in Maar Lake Sediments Response to Rapid Climate Change Since the Last Deglaciation</span>"</p>
Data archive for Towards understanding potential atmospheric contributions to abrupt climate changes: characterizing changes to the North Atlantic eddy-driven jet over the last deglaciation
<p>This archive contains simulated North Atlantic eddy-driven jet latitude and tilt data generated using the PlaSim model to accompany Andres and Tarasov (Climate of the Past, accepted), DOI: https://doi.org/10.5194/cp-15-1-2019.</p> <p>Paper abstract is as follows:</p> <p>"Abrupt climate shifts of large amplitudes were common features of the Earth’s climate as it transitioned into and out of the last full glacial state approximately 20 000<br> years ago, but their causes are not yet established. Midlatitude atmospheric dynamics may have played an important role in these climate variations through their effects on heat and precipitation distributions, sea ice extent, and wind-driven ocean circulation patterns. This study characterizes deglacial winter wind changes over the North Atlantic (NAtl) in a suite of transient deglacial simulations using the PlaSim Earth system model (run at T42 resolution) and the TraCE-<br> 21ka (T31) simulation. Though driven with yearly updates in surface elevation, we detect multiple instances of NAtl jet transitions in the PlaSim simulations that occur within 10<br> simulation years and a sensitivity of the jet to background climate conditions. Thus, we suggest that changes to the NAtl jet may play an important role in abrupt glacial climate changes.</p> <p>We identify two types of simulated wind changes over the last deglaciation. Firstly, the latitude of the NAtl eddy-driven jet shifts northward over the deglaciation in a sequence of distinct steps. Secondly, the variability in the NAtl jet gradually shifts from a Last Glacial Maximum (LGM) state with a strongly preferred jet latitude and a restricted latitudinal range to one with no single preferred latitude and a range that is at least 11 ◦ broader. These changes can significantly affect ocean circulation. Changes to the position of the NAtl jet alter the location of the wind forcing driving oceanic surface gyres and the limits of sea ice extent, whereas a shift to a more variable jet reduces the effectiveness of the wind forcing at driving surface ocean transports.</p> <p>The processes controlling these two types of changes differ on the upstream and downstream ends of the NAtl eddy-driven jet. On the upstream side over eastern North America, the elevated ice sheet margin acts as a barrier to the winds in both the PlaSim simulations and the TraCE-21ka experiment. This constrains both the position and the latitudinal variability in the jet at LGM, so the jet shifts in sync with ice sheet margin changes. In contrast, the downstream side over the eastern NAtl is more sensitive to the thermal state of the background climate. Our results suggest that the presence of an elevated ice sheet margin in the south-eastern sector of the North American ice complex strongly constrains the deglacial position of the jet over eastern North America and the western North Atlantic as well as its variability."</p> <p> </p>
Data compilation of stable and radiactive carbon, Nd isotopes, and Pa and Th of the Last Glacial Maxmium and last deglaciation
<p>Proxy data compilations of <span class="math-tex">\(\delta^{13}C\)</span>, benthic-planktic <sup>14</sup>C ages, Nd isotopes, and Pa and Th of the Last Glacial Maximum (LGM) and last deglaciation as used in Pöppelmeier et al. (2023).</p> <p>Pöppelmeier, F., Jeltsch-Thömmes, A., Lippold, J., Joos, F., & Stocker, T. F. (2023). Multi-proxy agreement on Atlantic circulation dynamics since the last ice age.</p>
Metabarcoding data reveal vertical multitaxa variation in topsoil communities during the colonization of deglaciated forelands
<p>Ice-free areas are increasing worldwide due to the dramatic glacier shrinkage and are undergoing rapid colonization by multiple lifeforms, thus representing key environments to study ecosystem development. Soils have a complex vertical structure. However, we know little about how microbial and animal communities differ across soil depths and development stages during the colonization of deglaciated terrains, how these differences evolve through time, and whether patterns are consistent among different taxonomic groups. Here, we used environmental DNA metabarcoding to describe how community diversity and composition of six groups (Eukaryota, Bacteria, Mycota, Collembola, Insecta, Oligochaeta) differ between surface (0-5 cm) and relatively deep (7.5-20 cm) soils at different stages of development across five Alpine glaciers. Taxonomic diversity increased with time since glacier retreat and with soil evolution; the pattern was consistent across different groups and soil depths. For Eukaryota, and particularly Mycota, alpha-diversity was generally the highest in soils close to the surface. Time since glacier retreat was a more important driver of community composition compared to soil depth; for nearly all the taxa, differences in community composition between surface and deep soils decreased with time since glacier retreat, suggesting that the development of soil and/or of vegetation tends to homogenize the first 20 cm of soil through time. Within both Bacteria and Mycota, several molecular operational taxonomic units were significant indicators of specific depths and/or soil development stages, confirming the strong functional variation of microbial communities through time and depth. The complexity of community patterns highlights the importance of integrating information from multiple taxonomic groups to unravel community variation in response to ongoing global changes.</p>
Deglacial climate changes as forced by different ice sheet reconstructions - model ouputs
<p>This dataset contains the model output corresponding to the paper entitled "Deglacial climate changes as forced by different ice sheet reconstructions" submitted to Climate of the Past. For the description of the model and simulations we refer to this article.</p> <p> </p> <p><strong>Simulations:</strong><br> degla_P_bathy_500yr_is_SH_nobathy = with ICE_6G_C, fixed bathymetry<br> degla_P_bathy_500yr_is_SH = with ICE_6G_C, evolving bathymetry<br> degla_P_bathy_500yr_is_SH_bis = with ICE_6G_C, evolving bathymetry, mask modified<br> degla_T_bathyT_100yr_is_SH_nobathy = with GLAC-1D, fixed bathymetry<br> degla_T_bathyT_100yr_is_SH = with GLAC-1D, evolving bathymetry<br> degla_T_bathyT_100yr_is_SH_FWF = with GLAC-1D, evolving bathymetry, fresh water flux<br> degla_T_bathyT_100yr_is_SH_FWFtest3 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 3<br> degla_T_bathyT_100yr_is_SH_FWFtest4 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 4</p> <p> </p> <p><strong>Variables and corresponding files:</strong><br> <em>Evolution of ocean volume (m3):</em><br> volume_ocean_degla_P_bathy_500yr_is_SH.txt<br> volume_ocean_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Evolution of ocean surface area (1e6 km2):</em><br> surface_area_degla_P_bathy_500yr_is_SH.txt<br> surface_area_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Sea land masks for time slices:</em><br> tmask_bathy_P_0yr_SH_CC_PI.nc<br> tmask_degla_P_bathy_500yr_is_SH_21ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_21ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_12ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_12ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_9ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_9ka.nc</p> <p><em>Evolution of global mean temperature (degree C):</em><br> Temperature_evolution_degla_P_bathy_500yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH_bis.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_500yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWF.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest3.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest4.txt</p> <p><em>Temperature maps for time slices:</em><br> temp_degla_P_bathy_500yr_is_SH_nobathy_21ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_21ka.nc<br> temp_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc</p> <p><em>Evolution of salinity:</em><br> iLOVECLIM_salinity_ICE-6G_C.nc<br> iLOVECLIM_salinity_GLAC-1D.nc</p> <p><em>Temperature evolution at NGRIP location:</em><br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Temperature evolution at EDC location:</em><br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Evolution of surface albedo (all globe):</em><br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of surface albedo (Northern Hemisphere):</em><br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_bis.nc</p> <p><em>Evolution of surface albedo (Southern Hemisphere):</em><br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Northern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Southern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Winter sea ice fraction and mixed layer depth (m) at time slices:</em><br> iLOVECLIM_sea_ice_mld_bathy_P_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_bathy_T_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_10ka.nc</p> <p><em>Evolution of the maximum strength of AMOC:</em><br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Meridional overtunring circulation at time slices:</em><br> MOC_degla_P_bathy_500yr_is_SH_21ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_10ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_21ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_10ka.nc</p>
Contrasting early successional dynamics of bacterial and fungal communities in recently deglaciated soils of the maritime Antarctic
Open the record for dataset details and reuse information.
Metabarcoding data reveal vertical multitaxa variation in topsoil communities during the colonization of deglaciated forelands
Open the record for dataset details and reuse information.
Caught in a bottleneck: Habitat loss for woolly mammoths in central North America and the ice-free corridor during the last deglaciation
Open the record for dataset details and reuse information.
Data from: Environment not dispersal limitation drives clonal composition of arctic Daphnia in a recently deglaciated area
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.