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ShareScore release 0.9.0
Dataset results
5 results for “invertebrate learning”
Figure 2 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 2. – Three images of organisms obtained by cropping images of lots; from left to right: Chalinidae (Porifera), Polyclinidae (Chordata), Hormatidae (Cnidaria).
Figure 3 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 3. – Image of a batch of macro-invertebrate bycatch organisms from Kerguelen Exclusive Economical Zone (Poker 4 survey, 2017), including corals, a crinoïd, an ophiurid, a sea urchin and a brachiopoda; organisms are incomplete and have been quickly spread out over a small plate to take the picture.
Figure 6 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 6. – Example of detection and classification obtained with an image including an Ophiuroid, a piece of coral and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 5 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 5. – Example of detection and classification obtained with an image including Ascidians and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 7 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 7. – Example of detection and classification obtained with an image including Crinoïds, a Gastropod and pieces of seaweed with network 2; red squares and annotations have been provided by the computer with no human action.
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.