Find research datasets worth reusing

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

opencc-by-4.0May 2022View details →

IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments"

IODP Expedition 382: Supplementary Tables for "Episodes of early Pleistocene West Antarctic Ice Sheet retreat recorded by Iceberg Alley sediments" Includes SEM QEMSCAN® and 40Ar/39Ar data for International Ocean Discovery Program (IODP) Expedition 382 Site U1538. Also includes a movie of a 3D-volume realization of an iceberg-rafted sedimentary layer from this site based on non-destructive X-ray microtomography imaging.   Data Set Captions:   Data Set S1. Modal mineralogy data based on QEMSCAN® analyses, which infer minerals from chemistry. The mineral name assignations for each chemistry-based category stated in this table are aided by visual (microscope-based) inspection of the raw sieved samples. Data Set S2. Mineral association data based on QEMSCAN® analyses. Please read data in columns, mineral against mineral (down then across left). These data define what touches what in the sample and is displayed as a percentage. Association refers to adjacency. Two minerals are “associated” if a pixel of one of the minerals occurs adjacent to a pixel of the other mineral. iExplorer software used scans the measured particles horizontally, from left to right, counting the associations that occur in the images (so the more pixels/closer the x-ray spacing the more accurate the data). Each column is independent. That is, it is split into a percentage of what touches what, so it is not expected that any two minerals’ data are reciprocal. The background category primarily reflects the free boundaries of ‘grains’ rather than liberated grains/particles. While it may provide an indicator of liberation, it does not represent liberation since it does not describe ‘particles’ which are made up of mineral grains. Inclusions and composite particles are therefore not described. Please consider the modal mineralogy (Tab. S1) when examining these mineral association data. Data Set S3. Lithotyping data based on QEMSCAN® analyses. Particles have been digitally filtered using a set of lithotype rules (also displayed in this data set). These rules are based on the mineral grains in the particles themselves and use their area percent within each particle and their size in microns. The lithotype names stated here are largely assigned based on the dominant mineral grain in each category. Data Set S4. 40Ar/39Ar ages of individual sand-sized hornblende and mica. See main text for method used to generate these ages. Data Set S5. Ties to place Hole U1538A NGR data on Dove Basin Stack (Reilly et al., 2021) depths. Movie S1. 3D-volume realization based on non-destructive X-ray microtomography imaging of a centimeter-scale iceberg-rafted debris-rich layer in Hole U1538A-36X-3W. 3D images were generated using a helical scanning trajectory that allows for long scan sequences and fast acquisition time. Based on the sample geometry, a voxel (pixel) resolution of ~14-μm was achieved. The 7000+ projection images were reconstructed to produce a 3D volume of image intensities (where higher values indicate greater x-ray attenuation). Avizo software was used for 3D segmentation and volume rendering to visualize gravel and sand to create this animation. The different colors assigned to each clast were chosen arbitrary.

opencc-by-4.0May 2024View details →

Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model

This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.   This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.

opencc-by-4.0Oct 2019View details →

Interannual iceberg meltwater fluxes over the Southern Ocean

Monthly Iceberg Meltwater Fluxes over the Southern Ocean (1972-2017) : Here is an update of the iceberg meltwater climatology initially provided by Merino et al (2016). This flux is still derived from NEMO's Lagrangian iceberg module developed by Marsh et al. (2015) and updated by Merino et al. (2016). It is run within a global ORCA025 ocean simulation running from 1958 to 2017, forced by the Drakkar Forcing Set (DFS-5.2; Dussin et al 2016). The 1958-1972 period is used to spin up the model, and the meltwater fluxes are provided over 1972-2017. The iceberg calving fluxes are constant, but their meltwater fluxes varies seasonally and interannually. Ice-shelf meltwater fluxes are reconstructed from glaciological observations (Merino et al. 2018), and here vary linearly from 1990 to 2010 (constant before and after).  Known caveats: <ul> <li>The ocean grid is the old "ORCA025" grid, which does not extend southward of 70°S, i.e. iceberg do not follow the southernmost ice shelf edges (e.g. Ronne ice shelf).</li> </ul> References: <ul> <li>Dussin, Raphael, Bernard Barnier, Laurent Brodeau, and Jean Marc Molines (2016). Drakkar Forcing Set DFS5.</li> <li>Marsh, R., Ivchenko, V. O., Skliris, N., Alderson, S., Bigg, G. R., Madec, G., and others (2015). NEMO-ICB (v1. 0): interactive icebergs in the NEMO ocean model globally configured at eddy-permitting resolution. Geoscientific Model Development, 8(5), 1547-1562.</li> <li>Merino, N., Le Sommer, J., Durand, G., Jourdain, N. C., Madec, G., Mathiot, P. and Tournadre, J. (2016). Antarctic icebergs melt over the Southern Ocean: Climatology and impact on sea ice. Ocean Modelling, 104, 99-110.</li> <li>Merino, N., Jourdain, N. C., Le Sommer, J., Goosse, H., Mathiot, P. and Durand, G. (2018). Impact of increasing antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. Ocean Modelling, 121, 76-89.</li> </ul>

opencc-by-4.0Jan 2023View details →

Research Iceberg

Figure 3 from <a href="https://doi.org/10.5281/zenodo.7320029">Removing Barriers to Reproducible Research in Archaeology</a>. Figure 3: The Research Iceberg, where only the article, preprint, and presentations on research are visible. The components of the research on which these visible outputs are based remain invisible (research questions, methods, data, mistakes and corrections, discussions, community consultation, documentation and ideas). Image by Esther Plomp. Please adapt and reuse for your own needs!

opencc-by-4.0Mar 2023View details →

Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s

This is the dataset created for the Journal of Glaciology paper "Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s" by Robert Headland, Nick Hughes and Jeremy Wilkinson (<a href="https://doi.org/10.1017/jog.2023.80">doi:10.1017/jog.2023.80</a>). We have endeavoured to make the data as accessible as possible by providing it in a range of formats.Please see the README.pdf for a detailed description of the files, and the paper for the dataset. Version 1.1 contains additional reports from newspaper archives.

opencc-by-4.0Mar 2017View details →

Simulated tracks and associated melting of 6912 small to giant Antarctic icebergs, September 1997 to December 2008

We present a dataset of Antarctic iceberg drift tracks and melting that includes small, medium-sized, and giant tabular icebergs with a realistic size distribution. An iceberg model is initialized with 6912 observed iceberg positions and sizes around Antarctica. The dataset is the result of a 2017 study "A simulation of small to giant Antarctic iceberg evolution: Differential impact on climatology estimates" published in JGR:Oceans (<a href="https://doi.org/10.1002/2016JC012513">https://doi.org/10.1002/2016JC012513</a>). We simulate drift and lateral melt using iceberg-draft averaged ocean currents, temperature, and salinity. A new basal melting scheme, originally applied in ice shelf melting studies, uses in situ temperature, salinity, and relative velocities at an iceberg's bottom. Climatology estimates of Antarctic iceberg melting based on simulations of small (≤2.2 km), “small-to-medium-sized" (≤10 km), and small-to-giant icebergs (including icebergs >10 km) exhibit differential characteristics: successive inclusion of larger icebergs leads to a reduced seasonality of the iceberg meltwater flux and a shift of the mass input to the area north of 58°S, while less meltwater is released into the coastal areas. This suggests that estimates of meltwater input solely based on the simulation of small icebergs introduce a systematic meridional bias; they underestimate the northward mass transport and are, thus, closer to the rather crude treatment of iceberg melting as coastal runoff in models without an interactive iceberg model. Future ocean simulations will benefit from the improved meridional distribution of iceberg melt, especially in climate change scenarios where the impact of iceberg melt is likely to increase due to increased calving from the Antarctic ice sheet.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 9 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 9 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_566-DJI_570 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 3 August 2019

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 6 August 2019

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 22 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_659-662 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_528, 530, 531-533 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_330, 332, 334, 335, 336, 337, 338, 339 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

IODP Expedition 382: Supplementary Tables for "New magnetostratigraphic insights from Iceberg Alley on the rhythms of Antarctic climate during the Plio-Pleistocene"

Supplementary tables for "New magnetostratigraphic insights from Iceberg Alley on the rhythms of Antarctic climate during the Plio-Pleistocene" Includes stratigraphic data for International Ocean Discovery Program (IODP) Expedition 382 Sites U1536 and U1537.   Table Captions: Table S1. Splice table and additional appended cores for Site U1536 used in this study.   Table S2. Splice table and additional appended cores for Site U1537 used in this study.   Table S3. Correlation table for creation of correlated equivalent depth (ced) scale between Sites U1536 and U1537. Table S4. Uncertainty estimates for Site U1536 natural gamma radiation (NGR) correlation to Site U1537 on mcd depth scale using Undatable (Lougheed & Obrochta, 2019).   Table S5. Site U1536 inclination, natural gamma radiation (NGR), gamma ray attenuation (GRA), and b* data used in this study. Table S6. Site U1537 inclination, natural gamma radiation (NGR), gamma ray attenuation (GRA), and b* data used in this study. Table S7. Meters below sea floor (mbsf) depths of magnetic reversals at Site U1536. Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005). Table S8. Meters composite depth (mcd) splice depths of magnetic reversals at Site U1536.  Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005). Table S9. Meters below sea floor (mbsf) depths of magnetic reversals at Site U1537. Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005). Table S10. Meters composite depth (mcd) splice depths of magnetic reversals at Site U1537.  Reversal ages are those used in this study’s age models (see Methods; Channell et al., 2016; Lisiecki & Raymo, 2005). Table S11. Magnetostratigraphic age model for Site U1536 generated with Undatable (Lougheed & Obrochta, 2019). Table S12. Magnetostratigraphic age model for Site U1537 generated with Undatable (Lougheed & Obrochta, 2019). Table S13. Dove Bain data stacks used in this study.   Table S14. Stratigraphic summary of magnetic reversals discussed in this study.  U1308 ages from Channell et al., 2016.  In relation to benthic δ18O, warm intervals are intervals with more positive values.  In relation to Dove Basin facies, warm intervals are intervals with high higher b*, lower NGR, and lower GRA.

opencc-by-4.0Jun 2015View details →

Figs 1, 2. M in Melting the iceberg: A new Megaselia Rondani species (Diptera: Phoridae) from Mali with the most striking wing ornament

Figs 1, 2. M. guentermuelleri sp. n., frontal setation (1) and left side of hypopygium (2). Scale bar = 0.1 mm.

opencc-by-4.0Jun 2015View details →

Figs 3–6 in Melting the iceberg: A new Megaselia Rondani species (Diptera: Phoridae) from Mali with the most striking wing ornament

Figs 3–6. Middle (3) and hind (4) tibiae, hind femur (5) and wing (6) of M. guentermuelleri sp. n. Photographs maY not reflect true colours observed in specimens; bristles oF the tb3 apical comb are enhanced. Scale bars = 0.1 mm.