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Fig. 4.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 4.3. Diplopods in UV fluorescence on top, in white light in the middle, in NIR at the bottom. UV fluorescence show that diplopods can fluoresce in different ways (blue, orange or not at all). NIR show the diplopods without the external pigmented layer.

Fig. 2.30. 3D in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.30. 3D model of a mobile Palaeolithic art from "le Trou des Nutons". Above: coloured surface model; middle: Surface without texture; below: image processed with automatic filtering method to highlight surface features of bison bone. The acquisition was made with a 5 Mpx RGB machine vision camera.

Fig. 3.12 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.12. Sitophilus oryzae (3.8mm body size) scanned with DISC3D. A. Scanning scheme with 398 camera positions. B. EDOF-image from the red camera position. C. Vcm-mesh (~250k polygones). D. Textured model.

Fig. 2.31 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.31. Perisama cardases captured with PLD. The normal colour image with relighting option is found on the upper left. Upper right is the grey scale image, bottom left the normal map and bottom right algorithmically-generated sketch using a filter.

Fig. 2.28. Picture for H in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.28. Picture for H-RTI digitisation. The highlights on the black spheres allow the algorithm to reconstruct a RTI model.

Fig. 3.35 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.35. Screen capture of the Mephisto scanning software while recombining scans of a stuffed African elephant produced with the Gotcha infrared sensor.

Fig. 3.32 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.32. Ishango rod digitised with NextEngine. https://sketchfab.com/models/555f0a85ca224ab88f3712272d877ce7

Fig. 3.31 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.31. Skull of Pan paniscus (RMCA) scanned with NextEngine. Left: the mesh with texture; right: the mesh without texture. https://sketchfab.com/models/38295c2ee9dd428f93134d0e97ffe851

Fig. 3.36. 3D in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.36. 3D model of the mammoth on display at the RBINS permanent exhibition. The bones were scanned one by one at the moment the skeleton was disassembled to move it to another exhibition spot. The Gotcha infrared depth sensor was used and the different 3D models were virtually reassembled in lhpFusionBox (ULB, Brussels). https://sketchfab.com/models/2d25256368a44a0fb98d0418ac500d47

Fig. 3.34 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 3.34. The Primesense infrared depth sensor of the Gotcha with a tripod, allowing it to stand or be used handheld while scanning.

Fig. 2.26. Plane 5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.26. Plane 5 of the specimen page at Zoosphere.net, showing the distribution of the specimen's species, as retrieved from GBIF. Image copyright MfN.

Fig. 2.24. Plane 3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.24. Plane 3 of the specimen page at Zoosphere.net, showing the taxonomy of the specimen. Image copyright MfN.

Fig. 2.23. Plane 2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.23. Plane 2 of the specimen page at Zoosphere.net, showing the specimen pictures. Image copyright MfN.

Fig. 2.11 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.11. Specimen stored within glycerin. Cleared and stained specimen of Haplochromis sp. pictured in glycerin. The right part of the picture is photographed with a magnification of 5×. Scale = 500 µm.

Fig. 2.20 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections

Fig. 2.20. Overview of the digitised specimen with the ZooSphere setup, visible at Zoosphere.net. Image copyright MfN.

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Cryo-OrbiSIMS for 3D molecular imaging of a bacterial biofilm in its native state

We developed a method for analysis and imaging of biological samples in their native state, by combining a cryo-OrbiSIMS instrument with cryogenic sample handling and high-pressure freezing. By using this method, we did analysis and imaging of frozen-hydrated mature Pseudomonas aeruginosa biofilm, which allows the identification and map of quorum sensing signaling molecules, nucleobases and bacterial membrane molecules with high spatial-resolution and high mass-resolution.  Some of quorum sensing signaling molecules were further confirmed by MS/MS. By comparing the analysis of frozen-hydrated Pseudomonas aeruginosa biofilm with the freeze-dried one,  we dicover that signal intensity of all interesting molecules get enhanced in the frozen-hydrated state. Especially for polar molecules, such as amino acid, it could even achieve 10,000 fold increasing. Here, we provide the original OrbiSIMS data including MS and MS/MS spectra, depth profile and images of frozen-hydrated and freeze-dried Pseudomonas aeruginosa biofilm. The data could be open by using SurfaceLab Version 7.0 (ION-TOF, Germany).

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 9 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 9 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_566-DJI_570 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 3 August 2019

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in the Vaigat Strait (NW Greenland) on 6 August 2019

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.

3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 22 August 2017

This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_659-662 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats.  A complete file list is provided in the README file that accompanies this dataset. This dataset is discussed in: Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to Remote Sensing.