Updated Jan 17, 2024 to include estimates through calendar year 2024.These files contain an estimate of the Atlantic Meridional Overturning Circulation (AMOC) volume and heat transports, computed using observations of temperature, salinity and subsurface velocity from the Argo array of profiling floats (DOI: 10.17882/42182#116315), and satellite-based observations of sea level from altimetry (DOI: 10.48670/moi-00148 and DOI: 10.48670/moi-00149).  The estimates are computed using the techniques of Willis (2010) and Hobbs and Willis (2012). In addition, estimates of wind stress at the surface were estimated from European Center for Medium Range Weather Forecast, ERA5 analysis (DOI: 10.24381/cds.143582cf).Note that in all files, although there are 12 time-steps per year, each time step represents a 3-month average, so the time series is over sampled.The .txt file contains comma separated values of the time series, with 1 header line and the following columns, estimated as in Willis (2010) and Hobbs and Willis (2012): Column 1: Decimal yearColumn 2: Ekman Volume Transport (Sverdrups)Column 3: Northward Geostrophic Transport (Sverdrups)Column 4: Meridional Overturning Volume Transport (Sverdrups)Column 5: Meridional Overturning Heat Transport (PetaWatts)The file called “trans_Argo_ERA5.nc” contains an estimate of the geostrophic transport as a function of latitude, longitude, depth and time, for the upper 2000 m for latitudes near 41 N in the Atlantic Ocean, estimated as described in Willis (2010). Also included are Ekman Transport and Overturning Transport as functions of time and latitude for this region.The file called “Q_ARGO_obs_dens_2000depth_ERA5.nc” contains estimates of heat transport for these regions based on various assumptions about the temperature of the ocean at depths unmeasured by the Core Argo array (depths below 2000m), estimated as described in Hobbs and Willis (2012).  These assumptions are described in the variable “Hpar”. If you use these data please cite:Willis, J. K., and Hobbs, W. R., Atlantic Meridional Overturning Circulation Near 41N from Altimetry and Argo Observations. Dataset access [YYYY-MM-DD] at 10.5281/zenodo.8170366. References & Acknowledgements:Hobbs, W. R., and J. K. Willis (2012), Midlatitude North Atlantic heat transport: A time series based on satellite and drifter data. J. Geophys. Res., 117, C01008, doi:10.1029/2011JC007039.Willis, J. K. (2010), Can in situ floats and satellite altimeters detect long-term changes in Atlantic Ocean overturning?, Geophys.  Res. Lett., 37, L06602, doi:10.1029/2010GL042372. http://www.agu.org/pubs/crossref/2010/2010GL042372.shtmlThis study has been conducted using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00149  and https://doi.org/10.48670/moi-00148 These data were collected and made freely available by the International Argo Program and the national programs that contribute to it.  (https://argo.ucsd.edu,  https://www.ocean-ops.org).  The Argo Program is part of the Global Ocean Observing System. “Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182#116315Hersbach, H., et al. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: 10.24381/cds.143582cf  (Accessed on 24-Dec-2022)

Updated Jan 17, 2024 to include estimates through calendar year 2024.These files contain an estimate of the Atlantic Meridional Overturning Circulation (AMOC) volume and heat transports, computed using observations of temperature, salinity and subsurface velocity from the Argo array of profiling floats (DOI: 10.17882/42182#116315), and satellite-based observations of sea level from altimetry (DOI: 10.48670/moi-00148 and DOI: 10.48670/moi-00149).  The estimates are computed using the techniques of Willis (2010) and Hobbs and Willis (2012). In addition, estimates of wind stress at the surface were estimated from European Center for Medium Range Weather Forecast, ERA5 analysis (DOI: 10.24381/cds.143582cf).Note that in all files, although there are 12 time-steps per year, each time step represents a 3-month average, so the time series is over sampled.The .txt file contains comma separated values of the time series, with 1 header line and the following columns, estimated as in Willis (2010) and Hobbs and Willis (2012): Column 1: Decimal yearColumn 2: Ekman Volume Transport (Sverdrups)Column 3: Northward Geostrophic Transport (Sverdrups)Column 4: Meridional Overturning Volume Transport (Sverdrups)Column 5: Meridional Overturning Heat Transport (PetaWatts)The file called “trans_Argo_ERA5.nc” contains an estimate of the geostrophic transport as a function of latitude, longitude, depth and time, for the upper 2000 m for latitudes near 41 N in the Atlantic Ocean, estimated as described in Willis (2010). Also included are Ekman Transport and Overturning Transport as functions of time and latitude for this region.The file called “Q_ARGO_obs_dens_2000depth_ERA5.nc” contains estimates of heat transport for these regions based on various assumptions about the temperature of the ocean at depths unmeasured by the Core Argo array (depths below 2000m), estimated as described in Hobbs and Willis (2012).  These assumptions are described in the variable “Hpar”. If you use these data please cite:Willis, J. K., and Hobbs, W. R., Atlantic Meridional Overturning Circulation Near 41N from Altimetry and Argo Observations. Dataset access [YYYY-MM-DD] at 10.5281/zenodo.8170366. References & Acknowledgements:Hobbs, W. R., and J. K. Willis (2012), Midlatitude North Atlantic heat transport: A time series based on satellite and drifter data. J. Geophys. Res., 117, C01008, doi:10.1029/2011JC007039.Willis, J. K. (2010), Can in situ floats and satellite altimeters detect long-term changes in Atlantic Ocean overturning?, Geophys.  Res. Lett., 37, L06602, doi:10.1029/2010GL042372. http://www.agu.org/pubs/crossref/2010/2010GL042372.shtmlThis study has been conducted using E.U. Copernicus Marine Service Information; https://doi.org/10.48670/moi-00149  and https://doi.org/10.48670/moi-00148 These data were collected and made freely available by the International Argo Program and the national programs that contribute to it.  (https://argo.ucsd.edu,  https://www.ocean-ops.org).  The Argo Program is part of the Global Ocean Observing System. “Argo (2000). Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE. https://doi.org/10.17882/42182#116315Hersbach, H., et al. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: 10.24381/cds.143582cf  (Accessed on 24-Dec-2022)

The dataset consists of three files: element-homogeneity.xlsx  -  results from six aerosol samples that were each divided into eight subsamples. All subsamples were analysed for 14 trace elements after strong acid digestion, and the results used to assess the homogeneity of trace element distribution over the aerosol filter surface. mass-blanksubt-precis.xls  -  masses of soluble trace elements determined in 33 aerosol samples using a total of eight different leaching procedures by six different laboratory groups. Total trace element masses for the same samples are also included. The trace elements measured vary between laboratory groups. Masses given have been blank-corrected and precisions are listed in most cases. mass-method-comparison-stats-v2.xls  -  summaries of the statistical tests used to compare the results obtained by each of the combinations of leaching method pairs produced from the study. Results are shown for all of the 26 intercomparison samples and for two of the aerosol source-based subsets of these samples. Note, this file is updated from the original submission, with revised correlation analysis.

Migration is the synchronised movement of a large part of a population from breeding to non-breeding grounds driven by seasonal variation of resources and avoidance of harsh winter conditions. Migration is a central component of many species’ life histories, including birds, mammals, fishes, and invertebrates. However, the interplay of ecological and evolutionary drivers of migration has long intrigued biologists and remains contentious. Shorebirds represent a valuable group for testing multiple predictors of migration, as they demonstrate a range of morphological and ecological characteristics (e.g., wing shape and habitat breadth), and a large proportion of shorebird species migrate. Here we tested whether breeding site climate, wing shape, body mass, and number of habitats occupied can predict migration across 196 shorebird species using novel Bayesian regression modelling allowing explicit decomposition of trait correlations into both phylogenetic and non-phylogenetic components. Increasing climate seasonality and pointier wing shapes favouring dispersal appeared strongly associated with migration, matching our predictions and potentially reflecting resource availability optimisation and the energetic costs of migration. Higher number of habitats occupied also appeared associated with migration, perhaps reflecting selection to decrease the specific habitat requirements of migration transits. The lack of a significant relationship for body mass may reflect conflicting selection pressures, as migration efficiency (energetics) increases with body size but migration duration (and time that can be spent at breeding sites) decreases.

Migration is the synchronised movement of a large part of a population from breeding to non-breeding grounds driven by seasonal variation of resources and avoidance of harsh winter conditions. Migration is a central component of many species’ life histories, including birds, mammals, fishes, and invertebrates. However, the interplay of ecological and evolutionary drivers of migration has long intrigued biologists and remains contentious. Shorebirds represent a valuable group for testing multiple predictors of migration, as they demonstrate a range of morphological and ecological characteristics (e.g., wing shape and habitat breadth), and a large proportion of shorebird species migrate. Here we tested whether breeding site climate, wing shape, body mass, and number of habitats occupied can predict migration across 196 shorebird species using novel Bayesian regression modelling allowing explicit decomposition of trait correlations into both phylogenetic and non-phylogenetic components. Increasing climate seasonality and pointier wing shapes favouring dispersal appeared strongly associated with migration, matching our predictions and potentially reflecting resource availability optimisation and the energetic costs of migration. Higher number of habitats occupied also appeared associated with migration, perhaps reflecting selection to decrease the specific habitat requirements of migration transits. The lack of a significant relationship for body mass may reflect conflicting selection pressures, as migration efficiency (energetics) increases with body size but migration duration (and time that can be spent at breeding sites) decreases.

Migration is the synchronised movement of a large part of a population from breeding to non-breeding grounds driven by seasonal variation of resources and avoidance of harsh winter conditions. Migration is a central component of many species’ life histories, including birds, mammals, fishes, and invertebrates. However, the interplay of ecological and evolutionary drivers of migration has long intrigued biologists and remains contentious. Shorebirds represent a valuable group for testing multiple predictors of migration, as they demonstrate a range of morphological and ecological characteristics (e.g., wing shape and habitat breadth), and a large proportion of shorebird species migrate. Here we tested whether breeding site climate, wing shape, body mass, and number of habitats occupied can predict migration across 196 shorebird species using novel Bayesian regression modelling allowing explicit decomposition of trait correlations into both phylogenetic and non-phylogenetic components. Increasing climate seasonality and pointier wing shapes favouring dispersal appeared strongly associated with migration, matching our predictions and potentially reflecting resource availability optimisation and the energetic costs of migration. Higher number of habitats occupied also appeared associated with migration, perhaps reflecting selection to decrease the specific habitat requirements of migration transits. The lack of a significant relationship for body mass may reflect conflicting selection pressures, as migration efficiency (energetics) increases with body size but migration duration (and time that can be spent at breeding sites) decreases.

These datasets are supplementary material to the publication from Lösing et al. (2025): Community Heat Flow Recommendations: Suitable Basal Boundary Conditions for Greenland and Antarctica in ISMIP7. When using these data, please cite the original datasets and associated publications, as well as Lösing et al. (2025). Input datasets AntarcticaData taken from Lösing & Ebbing (2021a,b); native resolution 55kmData taken from Stål et al., (2020, 2021); native resolution 20 km; The original Aq1 dataset from Stål et al. (2020, 2021), including uncertainty metrics, was cropped at the coastline and grounding line due to the extent of some observables used. To facilitate regridding, we applied a buffered nearest neighbour extrapolation technique to create a processed version of the dataset. We created a spatial buffer extending 4 grid cells (equivalent to 80 km given the 20×20 km resolution) around the valid values in Aq1 using morphological dilation with a circular kernel. We then applied nearest neighbour extrapolation to fill all NaN values across every variable in the dataset, followed by masking to retain only the extrapolated values within the 80 km buffer zone. This approach preserves the original model while adding a controlled "corona" of extrapolated values that extends smoothly into previously undefined regions, reducing edge effects and discontinuities that could occur during regridding procedures.GreenlandData taken from Colgan & Wansing (2021), Colgan et al. (2022); native resolution 55 kmOutput datasets“GHF_Regridded.zip” - Re-gridded datasets to 500 m resolution for Antarctica and to 500 m and 150 m resolution for Greenland using bilinear interpolation, with respective uncertainties. “GHF_Topographically_Corrected.zip” - Topographically corrected, re-gridded datasets (Antarctica: 500 m resolution; Greenland: 500 m and 150 m resolution) without uncertainties. Geothermal heat flow values are provided in W/m2. The grid resolutions of 500 m for Antarctica and 150 m for Greenland were chosen to correspond with the resolution of the topographic correction datasets as well as with the respective BedMachine datasets (Antarctica v3: Morlighem (2020), Morlighem et al., (2022a); Greenland v5: Morlighem et al. (2017, 2022b)). However, we also include a file for Greenland that has been regridded  to 500m and topographically corrected for completeness. “GHF_Original_Data.zip” and “Topographic_Correction.zip” - For quality control we also provide the input datasets with respective uncertainties, and the topographic correction datasets for Greenland and Antarctica (Colgan et al., 2021b)References:Colgan, William; Wansing, Agnes, (2021): Greenland Geothermal Heat Flow Database and Map. GEUS Dataverse, V2. https://doi.org/10.22008/FK2/F9P03LColgan, W., Wansing, A., Mankoff, K., Lösing, M., Hopper, J., Louden, K., Ebbing, J., Christiansen, F. G., Ingeman-Nielsen, T., Liljedahl, L. C., MacGregor, J. A., Hjartarson, Á., Bernstein, S., Karlsson, N. B., Fuchs, S., Hartikainen, J., Liakka, J., Fausto, R. S., Dahl-Jensen, D., Bjørk, A., Naslund, J.-O., Mørk, F., Martos, Y., Balling, N., Funck, T., Kjeldsen, K. K., Petersen, D., Gregersen, U., Dam, G., Nielsen, T., Khan, S. A. & Løkkegaard, A. (2022): Greenland Geothermal Heat Flow Database and Map (Version 1). Earth Syst. Sci. Data14, 2209–2238. https://doi.org/10.5194/essd-14-2209-2022Colgan, William, Joseph A. MacGregor, Kenneth D. Mankoff, Ryan Haagenson, Harihar Rajaram, Yasmina M. Martos, Mathieu Morlighem, Mark A. Fahnestock, and Kristian K. Kjeldsen. (2021b): Topographic correction of geothermal heat flux in Greenland and Antarctica." Journal of Geophysical Research: Earth Surface 126, no. 2. e2020JF005598. doi: 10.1029/2020JF005598Lösing, Mareen; Ebbing, Jörg (2021a): Predicted Antarctic Heat Flow and Uncertainties using Machine Learning [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.930237. https://doi.org/10.1594/PANGAEA.930237Lösing, M., & Ebbing, J. (2021b): Predicting geothermal heat flow in Antarctica with a machine learning approach. Journal of Geophysical Research: Solid Earth, 126(6), e2020JB021499. https://doi.org/10.1029/2020JB021499Morlighem, M., Rignot, E., Binder, T., Blankenship, D. D., Drews, R., Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm, V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson, N. B., Lee, W., Matsuoka, K., Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J. L., Rosier, S., Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., van den Broeke, M. R., van Ommen, T., van Wessem, M. & Young, D. A.. (2020): Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience. 13. DOI: 10.1038/s41561-019-0510-8.Morlighem, M. (2022a): MEaSUREs BedMachine Antarctica. (NSIDC-0756, Version 3). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/FPSU0V1MWUB6  Morlighem, M., Williams, C., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., Catania, G., Chauché, N., Dowdeswell, J. A., Dorschel, B., Fenty, I., Hogan, K., Howat, I., Hubbard, A., Jakobsson, M., Jordan, T. M., Kjeldsen, K. K., Millan, R., Mayer, L., Mouginot, J., Noël, B., O'Cofaigh, C., Palmer, S. J., Rysgaard, S., Seroussi, H., Siegert, M. J., Slabon, P., Straneo, F., van den Broeke, M. R., Weinrebe, W., Wood, M. & Zinglersen, K.. (2017): BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multi-beam echo sounding combined with mass conservation. Geophysical Research Letters. 44. DOI: 10.1002/2017GL074954.Morlighem, M. et al. (2022b): IceBridge BedMachine Greenland. (IDBMG4, Version 5). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/GMEVBWFLWA7X. Stål, T.,  Reading, A. M., Halpin, J. A., Whittaker, J. M.  (2020): Antarctic geothermal heat flow model: Aq1 [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.924857.Stål, T., Reading, A. M., Halpin, J. A., & Whittaker, J. M. (2021): Antarctic geothermal heat flow model: Aq1. Geochemistry, Geophysics, Geosystems, 22(2), e2020GC009428. https://doi.org/10.1029/2020GC009428

These datasets are supplementary material to the publication from Lösing et al. (2025): Community Heat Flow Recommendations: Suitable Basal Boundary Conditions for Greenland and Antarctica in ISMIP7. When using these data, please cite the original datasets and associated publications, as well as Lösing et al. (2025). Input datasets AntarcticaData taken from Lösing & Ebbing (2021a,b); native resolution 55kmData taken from Stål et al., (2020, 2021); native resolution 20 km; The original Aq1 dataset from Stål et al. (2020, 2021), including uncertainty metrics, was cropped at the coastline and grounding line due to the extent of some observables used. To facilitate regridding, we applied a buffered nearest neighbour extrapolation technique to create a processed version of the dataset. We created a spatial buffer extending 4 grid cells (equivalent to 80 km given the 20×20 km resolution) around the valid values in Aq1 using morphological dilation with a circular kernel. We then applied nearest neighbour extrapolation to fill all NaN values across every variable in the dataset, followed by masking to retain only the extrapolated values within the 80 km buffer zone. This approach preserves the original model while adding a controlled "corona" of extrapolated values that extends smoothly into previously undefined regions, reducing edge effects and discontinuities that could occur during regridding procedures.GreenlandData taken from Colgan & Wansing (2021), Colgan et al. (2022); native resolution 55 kmOutput datasets“GHF_Regridded.zip” - Re-gridded datasets to 500 m resolution for Antarctica and to 500 m and 150 m resolution for Greenland using bilinear interpolation, with respective uncertainties. “GHF_Topographically_Corrected.zip” - Topographically corrected, re-gridded datasets (Antarctica: 500 m resolution; Greenland: 500 m and 150 m resolution) without uncertainties. Geothermal heat flow values are provided in W/m2. The grid resolutions of 500 m for Antarctica and 150 m for Greenland were chosen to correspond with the resolution of the topographic correction datasets as well as with the respective BedMachine datasets (Antarctica v3: Morlighem (2020), Morlighem et al., (2022a); Greenland v5: Morlighem et al. (2017, 2022b)). However, we also include a file for Greenland that has been regridded  to 500m and topographically corrected for completeness. “GHF_Original_Data.zip” and “Topographic_Correction.zip” - For quality control we also provide the input datasets with respective uncertainties, and the topographic correction datasets for Greenland and Antarctica (Colgan et al., 2021b)References:Colgan, William; Wansing, Agnes, (2021): Greenland Geothermal Heat Flow Database and Map. GEUS Dataverse, V2. https://doi.org/10.22008/FK2/F9P03LColgan, W., Wansing, A., Mankoff, K., Lösing, M., Hopper, J., Louden, K., Ebbing, J., Christiansen, F. G., Ingeman-Nielsen, T., Liljedahl, L. C., MacGregor, J. A., Hjartarson, Á., Bernstein, S., Karlsson, N. B., Fuchs, S., Hartikainen, J., Liakka, J., Fausto, R. S., Dahl-Jensen, D., Bjørk, A., Naslund, J.-O., Mørk, F., Martos, Y., Balling, N., Funck, T., Kjeldsen, K. K., Petersen, D., Gregersen, U., Dam, G., Nielsen, T., Khan, S. A. & Løkkegaard, A. (2022): Greenland Geothermal Heat Flow Database and Map (Version 1). Earth Syst. Sci. Data14, 2209–2238. https://doi.org/10.5194/essd-14-2209-2022Colgan, William, Joseph A. MacGregor, Kenneth D. Mankoff, Ryan Haagenson, Harihar Rajaram, Yasmina M. Martos, Mathieu Morlighem, Mark A. Fahnestock, and Kristian K. Kjeldsen. (2021b): Topographic correction of geothermal heat flux in Greenland and Antarctica." Journal of Geophysical Research: Earth Surface 126, no. 2. e2020JF005598. doi: 10.1029/2020JF005598Lösing, Mareen; Ebbing, Jörg (2021a): Predicted Antarctic Heat Flow and Uncertainties using Machine Learning [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.930237. https://doi.org/10.1594/PANGAEA.930237Lösing, M., & Ebbing, J. (2021b): Predicting geothermal heat flow in Antarctica with a machine learning approach. Journal of Geophysical Research: Solid Earth, 126(6), e2020JB021499. https://doi.org/10.1029/2020JB021499Morlighem, M., Rignot, E., Binder, T., Blankenship, D. D., Drews, R., Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P., Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm, V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson, N. B., Lee, W., Matsuoka, K., Millan, R., Mouginot, J., Paden, J., Pattyn, F., Roberts, J. L., Rosier, S., Ruppel, A., Seroussi, H., Smith, E. C., Steinhage, D., Sun, B., van den Broeke, M. R., van Ommen, T., van Wessem, M. & Young, D. A.. (2020): Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nature Geoscience. 13. DOI: 10.1038/s41561-019-0510-8.Morlighem, M. (2022a): MEaSUREs BedMachine Antarctica. (NSIDC-0756, Version 3). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/FPSU0V1MWUB6  Morlighem, M., Williams, C., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., Catania, G., Chauché, N., Dowdeswell, J. A., Dorschel, B., Fenty, I., Hogan, K., Howat, I., Hubbard, A., Jakobsson, M., Jordan, T. M., Kjeldsen, K. K., Millan, R., Mayer, L., Mouginot, J., Noël, B., O'Cofaigh, C., Palmer, S. J., Rysgaard, S., Seroussi, H., Siegert, M. J., Slabon, P., Straneo, F., van den Broeke, M. R., Weinrebe, W., Wood, M. & Zinglersen, K.. (2017): BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multi-beam echo sounding combined with mass conservation. Geophysical Research Letters. 44. DOI: 10.1002/2017GL074954.Morlighem, M. et al. (2022b): IceBridge BedMachine Greenland. (IDBMG4, Version 5). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/GMEVBWFLWA7X. Stål, T.,  Reading, A. M., Halpin, J. A., Whittaker, J. M.  (2020): Antarctic geothermal heat flow model: Aq1 [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.924857.Stål, T., Reading, A. M., Halpin, J. A., & Whittaker, J. M. (2021): Antarctic geothermal heat flow model: Aq1. Geochemistry, Geophysics, Geosystems, 22(2), e2020GC009428. https://doi.org/10.1029/2020GC009428

This record contains:(i) prokaryote genomes binned from seawater metagenomes collected from 48 sites on the Great Barrier Reef(ii) Crassvirales genomes identified in metagenome assemblies(iii) Mamiellales genomes identified in metagenome assemblies(iv) predicted taxagroup contigs

This record contains:(i) prokaryote genomes binned from seawater metagenomes collected from 48 sites on the Great Barrier Reef(ii) Crassvirales genomes identified in metagenome assemblies(iii) Mamiellales genomes identified in metagenome assemblies(iv) predicted taxagroup contigs