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.
188
datasets available to search
ShareScore release 0.9.0
Dataset results
188 results for “Iceberg”
Fjord circulation induced by melting icebergs: datasets and code
<p>Datasets and code associated with the following paper:</p> <ul> <li>Hughes (2024) <a href="https://doi.org/10.5194/tc-18-1315-2024">Fjord circulation induced by melting icebergs</a>,<em>The Cryosphere</em></li> </ul> <p>This archive contains:</p> <ul> <li>The 'analytical model' written in Python (melt_induced_circulation.py) and scripts to recreate the results in Figures 7 and 8</li> <li>Outputs from three numerical simulations in netCDF form. In the filenames, <ul> <li>deltaS refers to the change in stratification from surface to seafloor (default in paper was 3)</li> <li>gamma_multiple refers to the factor applied to γS and γT (default in paper was 4)</li> <li>T0 refers to ambient temperature (default in paper was 2)</li> </ul> </li> <li>A zip directory containing the MITgcm configuration needed to recreate the numerical simulations including <ul> <li>A Python script to generate the inputs binary files</li> <li>An example of the inputs generated for one set of parameters</li> <li>Copies of the data.* configuration files</li> <li>The additional Fortran code that is not part of the official MITgcm code</li> </ul> </li> </ul> <p> </p>
Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"
<p>Supplementary Tables for "A 3.3-Million-Year Record of Antarctic Iceberg Rafted Debris and Ice Sheet Evolution Quantified by Machine Learning"</p> <p> </p> <p><strong>Table Captions:</strong></p> <p><strong>Table S1.</strong> Site U1537 Age Model Tie Points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S2. </strong>Site U1537 Age Model used in this study, applying both the age tie points from Weber et al. (2022) and Reilly et al. (2021)</p> <p><strong>Table S3. </strong>Hole U1538A correlation to the Dove Basin Stack from Bailey et al. (2022), and the addition of the U1538 splice CCSF-A depth to the Dove Basin CCSF-A</p> <p><strong>Table S4. </strong>Site U1538 splice table used in this study, note the continuation down Hole A after Core 14H</p> <p><strong>Table S5. </strong>New top core section offsets for Site U1536 cores added to the Reilly et al. (2021) extended splice table</p> <p><strong>Table S6. </strong>New top core section offsets for Site U1537 cores added to Reilly et al. (2021) extended splice table</p> <p><strong>Table S7. </strong>Comparison of Convolutional Neural Network IRD counts to shipboard eye counts of IRD at Site U1536</p> <p><strong>Table S8. </strong>Site U1537 CNN IRD Counts per 50 cm bins</p> <p><strong>Table S9. </strong>Site U1536 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S10. </strong>Site U1537 IRD Fluxes Per 5 kyr Quantified by a Convolutional Neural Network (0-3.3 Ma)</p> <p><strong>Table S11. </strong>Site U1536 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S12. </strong>Site U1537 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p> <p><strong>Table S13. </strong>Site U1538 IRD Fluxes Per 1 kyr Quantified by a Convolutional Neural Network (0-1.2 Ma)</p>
The Response of the Southern Ocean to Climatological Iceberg Freshwater Forcing
<p><strong>Data Availability Statement:</strong></p> <p>The data presented here are the model outputs for the study titled <em>"The Response of the Southern Ocean to Climatological Iceberg Freshwater Forcing."</em> Due to size limitations on data uploads, a coarser resolution dataset is provided. For access to the full-resolution dataset, please contact the corresponding author at <strong><a rel="noopener">jingwei.zhang@utas.edu.au</a></strong>.</p>
Physical processes controlling the rifting of Larsen C Ice Shelf, Antarctica, prior to the calving of iceberg A68 in 2017
Open the record for dataset details and reuse information.
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 16:56 UTC
<p>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 16:56 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_814-817 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. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 21:31 UTC
<p>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 21:31 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_767-773 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. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
FIGURE 36 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 36. Wings of Cladochaeta species. A. C. periotoi nov. sp. (holoype). B. C. phallotrixa nov. sp. (holotype). C. C. stigmata nov. sp. (holotype). Scale bar: 0.5mm.
FIGURE 34 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 34. Lateral habitus of Cladochaeta species, holotypes. A. C. atlantica nov. sp. B. C. periotoi nov. sp. C. C. phallotrixa nov. sp. D. C. stigmata nov. sp. Scale bars: 0.5mm.
FIGURE 33 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 33. Lateral habitus of Cladochaeta species, holotypes. A. C. bomplandi. B. C. arthrostyla. C. C. asapha nov. sp. D. C. chauliodactyla nov. sp. E. C. conicophallus nov. sp. F. C. dicrophallus. nov. sp. G. C. grimaldii nov. sp. Scale bars: 0.5mm.
FIGURE 35 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 35. Wings of Cladochaeta species. A. C. armata. B. C. armatopsis nov. sp. (holotype). C. C. balbiae nov. sp. (holotype). D. C. paraitinga nov. sp. (holotype). E. C. bomplandi. F. C. arthrostyla. G. C. asapha nov. sp. (holotype). H. C. chauliodactyla nov. sp. (holotype). I. C. conicophallus nov. sp. (holotype). J. C. dicrophallus nov. sp. (holotype). K. C. grimaldii nov. sp. (holotype). L. C. atlantica nov. sp. (holotype). Scale bars: 0.5mm.
FIGURE 32 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 32. Lateral habitus of Cladochaeta species. A. C. armata. B. C. armatopsis nov. sp. (holotype). C. C. balbiae nov. sp. (holotype). D. C. paraitinga nov. sp. (holotype). Scale bars: 0.5mm.
FIGURE 30 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 30. Cladochaeta stigmata, nov. sp., holotype. A. Head, dorsal view. B. Head, lateral view. C. Head, frontal view. D. Thorax, lateral view. E. Thorax, dorsal view. F. Abdomen, dorsal view. Scale bars: 0.1mm.
FIGURE 31 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 31. Cladochaeta stigmata, nov. sp., holotype, male terminalia. A, D. Terminal view. B, E. Terminal oblique view. C, F. Lateral view.
FIGURE 29 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 29. Cladochaeta phallotrixa, sp.nov. sp., holotype, male terminalia. A, C. Terminal view. B, D. Terminal oblique view.
FIGURE 28 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 28. Cladochaeta phallotrixa, sp.nov. sp., holotype. A. Head, dorsal view. B. Head, lateral view. C. Head, frontal view. D. Thorax, lateral view. E. Thorax, dorsal view. F. Abdomen, dorsal view. Scale bars: 0.1mm.
FIGURE 24 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 24. Cladochaeta atlantica nov. sp., paratype. A. Head, dorsal view. B. Head, lateral view. C. Head, frontal view. D. Thorax, lateral view. E. Thorax, dorsal view. F. Abdomen, dorsal view. Scale bars: 0.1mm.
FIGURE 22 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 22. Cladochaeta grimaldii, nov. sp., holotype. A. Head, dorsal view. B. Head, lateral view. C. Head, frontal view. D. Thorax, lateral view. E. Thorax, dorsal view. F. Abdomen, dorsal view. Scale bars: 0.1mm.
FIGURE 19 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 19. Cladochaeta conicophallus nov. sp., holotype, male terminalia. A, D. Terminal view. B, E. Terminal oblique view. C, F. Lateral view.
FIGURE 26 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 26. Cladochaeta periotoi, nov. sp., holotype. A. Head, dorsal view. B. Head, lateral view. C. Thorax, dorsal view. Scale bars: 0.1mm.
FIGURE 18 in Going beyond the tip of the Drosophilidae iceberg: New Cladochaeta Coquillett, 1900 (Diptera: Drosophilidae) from Brazil
FIGURE 18. Cladochaeta conicophallus nov. sp., holotype. A. Head, dorsal view. B. Head, lateral view. C. Head, frontal view. D. Thorax, lateral view. E. Thorax, dorsal view. Scale bars: 0.1mm.
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.