| _version_ | 1867170099534757888 |
|---|---|
| author | Klinkenbuß, Denise Metz, Olivia Reichert, Jessica Hauffe, Torsten Neubauer, Thomas A Wesselingh, Frank P Wilke, Thomas |
| author_facet | Klinkenbuß, Denise Metz, Olivia Reichert, Jessica Hauffe, Torsten Neubauer, Thomas A Wesselingh, Frank P Wilke, Thomas |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The dataset contains 3D models of fossil and subfossil specimens of four dreissenid species from the Caspian Sea, i.e., Dreissena polymorpha, D. elata, D. caspia and D. grimmi. In total 64 specimens (15 to 18 per species) of Late Pleistocene to Holocene deposits from locations in Russia (Selitrennoye, Turali) and Kazakhstan were included. The data were generated using the scanning software Artec Studio 11. See linked paper for detailed information about 3D model calculation. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_914822 |
| institution | PANGAEA |
| language | en |
| publishDate | 2020 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | 3D models for fossil and subfossil Dreissenidae shells from the Caspian Sea Klinkenbuß, Denise Metz, Olivia Reichert, Jessica Hauffe, Torsten Neubauer, Thomas A Wesselingh, Frank P Wilke, Thomas 3D models; Caspian Sea; Caspian_Sea; Caspian Sea; Dreissenidae; morphometry; Quaternary; machine learning The dataset contains 3D models of fossil and subfossil specimens of four dreissenid species from the Caspian Sea, i.e., Dreissena polymorpha, D. elata, D. caspia and D. grimmi. In total 64 specimens (15 to 18 per species) of Late Pleistocene to Holocene deposits from locations in Russia (Selitrennoye, Turali) and Kazakhstan were included. The data were generated using the scanning software Artec Studio 11. See linked paper for detailed information about 3D model calculation. |
| title | 3D models for fossil and subfossil Dreissenidae shells from the Caspian Sea |
| topic | 3D models; Caspian Sea; Caspian_Sea; Caspian Sea; Dreissenidae; morphometry; Quaternary; machine learning |
| url | https://doi.org/10.1594/PANGAEA.914822 |