https://doi.org/10.25678/000A0R
Aquascope October 2019
Dataset extent
Data and Resources
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images_5p0xMAG_oct2019.tar.gzTAR
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images_0p5xMAG_oct2019.tar.gzTAR
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class_and_feat_5p0xMAG_oct2019.tar.gzTAR
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class_and_feat_0p5xMAG_oct2019.tar.gzTAR
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README.txtTXT
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raw_data_5p0xMAG_oct2019.tar.gzTAR
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raw_data_0p5xMAG_oct2019_1-5.tar.gzTAR
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raw_data_0p5xMAG_oct2019_11-15.tar.gzTAR
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raw_data_0p5xMAG_oct2019_16-18.tar.gzTAR
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raw_data_0p5xMAG_oct2019_19-21.tar.gzTAR
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raw_data_0p5xMAG_oct2019_22-24.tar.gzTAR
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raw_data_0p5xMAG_oct2019_25-27.tar.gzTAR
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raw_data_0p5xMAG_oct2019_28-end.tar.gzTAR
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LICENSE
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LICENSE.txtTXT
Citation
Dennis, S., Merz, E., Reyes, M., Merkli, S., Baity Jesi, M., Kyathanahally, S., et al. (2023). Aquascope October 2019 (Version 1.0). Eawag: Swiss Federal Institute of Aquatic Science and Technology. https://doi.org/10.25678/000A0R
Metadata
Author |
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Keywords | phytoplankton images,zooplankton images,plankton classification,machine learning,time series,image features,plankton communities |
Taxa (scientific names) |
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Organisms (generic terms) |
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Timerange |
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Geographic Name(s) |
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Review Level | domain specific |
Curator | Dennis, Stuart |
Contact | Pomati, Francesco <Francesco.Pomati@eawag.ch> |
DOI | 10.25678/000A0R |