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Fig. 2.12 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.12. General view of the 'Information in Images' system mounted on a Leica microscope at the Royal Museum for Central Africa.

Fig. 2.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.4. Picturing pinned insects. The pictures represent several insects with highly reflective surfaces positioned in different ways (pinned, upper pictures; glued, bottom pictures). No shadows, no overexposed areas and hardly any reflections are present. Scale = 1 mm.

Fig. 2.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.3. Picturing shiny metallic specimens: Left: A manganite specimen (scale = 1 cm); Right: a detailed view of the manganite specimen (scale = 1 mm).

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Combining Natural Language and Images for Garbage Classification: A Public Benchmark

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Figure 2 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187

Figure 2 In the Central Library of Datasets, natural history collection staff will find correctly identified images of their target organisms and download the data for training of an individually customized classifier (photos: Lepidoptera by Entomological Collection of ETH Zürich; Orthoptera by Naturalis Biodiversity Center; Brassicaceae by United Herbaria Z+ZT, ZT-00164967, ZT-00167494, ZT-00171530, CC BY-SA 4.0). The current figure shows a mock-up.

Figure 3 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187

Figure 3 Sharing of taxonomic knowledge between institutes. (1) Each algorithm contains two basic components: the feature extractor and the classifier. (2) The Central Library of Datasets allows the user to browse through all available images of collection objects; (3) based on all available images, a regularly updated central feature extractor is created and published; (4) custom made algorithms can relatively easily be created by building a classifier based on a selection of taxa from the central library and combining this with the central feature extractor; (5) newly created algorithms together with their metadata (probability & information on content) are published through a web service in the Central Library of Algorithms (6) and can be used through the Identification web services (API) either for batch processing of images or through a mobile app. Models can be easily extended by other institutions by combining data sources (7).

Figure 1 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187

Figure 1 In the Central Library of Algorithms, natural history collection staff will select algorithms (feature extractors, models, etc.) that are most appropriate for the identification of their target organisms and add them to the workbench. The current figure shows a mock-up.

Figure 6 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187

Figure 6 Mock-up of an interface for automated taxon identification. Naturalis holds over 500.000 specimens of unmounted, unsorted and often unidentified, papered butterflies and moths that were collected mostly in Europe and Asia over the past 200 years. In early 2016, Naturalis embarked on a 10-year-project to digitally identify all these specimens with the help of dedicated volunteers (Caspers et al. 2019). Specimens are unpacked, photographed, had their label data registered and then repacked, still unmounted, for long-term storage. Specimen images were then dragged and dropped into a web-based interface to get a near-instant response with multiple predictions about the taxonomic identity including probability values.

Figure 5 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187

Figure 5 Non-expert collection staff easily find and afterwards sort specimens by taxon (line color) and by accuracy of the identification (line type). The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.

Figure 4 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187

Figure 4 Algorithms recognize and number individual specimens in a drawer of unsorted items. The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.

Figure 2 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416

Figure 2 Fluid material scanning box: A dimensions of top-down and cross-section view of box plans, and finished box with glass affixed in B top and C bottom view.

Figure 3 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416

Figure 3 Scanning process images: A scanning process with scan view setup, box covers, full scan, and cropped image B required fill levels of fluid for box covers to prevent bubbles and standoffs (left) to prevent an uneven scan (right) C six boxes set up on scanner; and D resulting full scan. Clear Plexiglas cube standoffs B and slip-joint rings D (lower-right) improve scans.

Figure 4 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416

Figure 4 Cropped scan and scan resolution comparison: A final cropped scan of EMEC 37259, Psychoglyphaormiae (Ross, 1938) ♂, with standard layout of labels and specimens B–D comparison of scan resolution settings of 600 dpi, 1200 dpi, and 2400 dpi to show 100% scale display quality differences. Black scale bar is 5 mm for Rhyacophilaharmstoni Ross, 1944 ♂ (EMEC 41255, NV: White Pine Co.) in all images. Images are unmodified in software (e.g., no sharpening, white balance, or color correction).

Figure 1 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416

Figure 1 Fluid-stored arthropods in A a museum jar with shell vials B shell vials in a jar rack C racks in storage shelves at the Essig Museum of Entomology. Other fluid-based storage approaches include D screw-top scintillation vials and stoppered vials. Individually capped vials are commonly stored in racks on shelves.

Figure 5 from: Mendez PK, Lee S, Venter CE (2018) Imaging natural history museum collections from the bottom up: 3D print technology facilitates imaging of fluid-stored arthropods with flatbed scanners. ZooKeys 795: 49-65. https://doi.org/10.3897/zookeys.795.28416

Figure 5 Example scans of different species and life stages of Essig Museum of Entomology Trichoptera showing detail of morphological features ADolophilodesnovusamericana (Ling, 1938) ♂, CA: Marin Co., EMEC 373433 BWormaldia sp. larvae, CA: Nevada Co., EMEC 1194742 CLimnephilusfrijole Ross, 1944 genitalic dissections of ♀♂, CA: Modoc Co., EMEC 373223 D larva and E case of Yphriacalifornica (Banks, 1907), CA: El Dorado Co., EMEC 373355 FLimnephilidae pupa, no location data, EMEC 373316 GPsychoglyphaormiae ♀ (Ross, 1938), CA: Nevada Co., EMEC 373259 HPsychoglypha sp. ♀, CA: Nevada Co., EMEC 373266 and IHesperophylaxdesignatus (Walker, 1852) case, CA: Mono Co., EMEC 373246. All scans are unmodified in software (no sharpening, white balance, or color correction). Scale bar: 5mm.

Fig. 8.1 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 8.1. Sketchfab page of RMCA.

Fig. 8.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 8.2. Annotated model of a grass snake skull on Sketchfab. https://skfb.ly/6tNwY

Fig. 6.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 6.3. Back view of the skull digitised with different scanners.

Fig. 6.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 6.4. Parietal view of the skull digitised with different scanners.

Fig. 6.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 6.5. Sea urchin captured by 3 different structured light scanners and by SfM.