Pinned insect images and corresponding label outlines in JSON format. Part of this dataset contains the image set https://doi.org/10.5281/zenodo.3243225. This dataset is used for training a machine learning model for the identification and segmentation of labels from pinned specimens for the ALICE project: https://doi.org/10.31219/osf.io/s2p73
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page...
A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35...
Accurate species identification is the basis for all aspects of taxonomic research and is an essenti...
Insects make up over 70% of the world's known species (Resh and Carde 2009). This is well represente...
This paper introduces an automated method for the identification of chironomid larvae mounted on mic...
The world's natural history collections contain at least 2 billion specimens (Ariño 2010), represent...
Techniques for image recognition through machine learning have advanced rapidly over recent years an...
Digitisation of natural history collections draws increasing attention. The digitised specimens not ...
Over one billion people in developing countries are afflicted by parasitic infections caused by soil...
Digitized herbarium images contain complex information unrelated to the shape and color of the speci...
This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species us...
Image-crops of specimens from insect drawers In 2017 we scanned 208 insect drawers containing the c...
This report describes the state of art and work in progress on automated methods in mass-digitisatio...
Automated identification of insects is a tough task where many challenges like data limitation, imba...
Collection and preparation of empirical data still represent one of the most important, but also exp...
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page...
A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35...
Accurate species identification is the basis for all aspects of taxonomic research and is an essenti...
Insects make up over 70% of the world's known species (Resh and Carde 2009). This is well represente...
This paper introduces an automated method for the identification of chironomid larvae mounted on mic...
The world's natural history collections contain at least 2 billion specimens (Ariño 2010), represent...
Techniques for image recognition through machine learning have advanced rapidly over recent years an...
Digitisation of natural history collections draws increasing attention. The digitised specimens not ...
Over one billion people in developing countries are afflicted by parasitic infections caused by soil...
Digitized herbarium images contain complex information unrelated to the shape and color of the speci...
This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species us...
Image-crops of specimens from insect drawers In 2017 we scanned 208 insect drawers containing the c...
This report describes the state of art and work in progress on automated methods in mass-digitisatio...
Automated identification of insects is a tough task where many challenges like data limitation, imba...
Collection and preparation of empirical data still represent one of the most important, but also exp...
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page...
A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35...
Accurate species identification is the basis for all aspects of taxonomic research and is an essenti...
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