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TRENDMD RECOMMENDS

 1. Next-generation morphological character discovery and evaluation: an X-ray
    micro-CT enhanced revision of the ant genus Zasphinctus Wheeler
    (Hymenoptera, Formicidae, Dorylinae) in the Afrotropics
    Francisco Hita Garcia et al., ZooKeys, 2017
 2. Systematics of the ant genus Proceratium Roger (Hymenoptera, Formicidae,
    Proceratiinae) in China – with descriptions of three new species based on
    micro-CT enhanced next-generation-morphology
    Michael Staab et al., ZooKeys, 2018
 3. Photo images, 3D models and CT scanned data of loaches (Botiidae, Cobitidae
    and Nemacheilidae) of Japan
    Yuichi Kano et al., Biodiversity Data Journal, 2018

 1. System uses ordinary light to reproduce 3D surfaces
    Sensor Review, 1998
 2. Crowdsourcing 3D cultural heritage: best practice for mass photogrammetry
    Eugene Ch’ng et al., Journal of Cultural Heritage Management and Sustainable
    Development, 2019
 3. Be Vivid: Bimekizumab versus ustekinumab for the treatment of moderate to
    severe plaque psoriasis (BE VIVID): efficacy and safety from a 52-week,
    multicentre, double-blind, active comparator and placebo controlled phase 3
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    Kristian Reich et al., The Lancet, 2021

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Research Idea
Research Ideas and Outcomes 8: e86985
https://doi.org/10.3897/rio.8.e86985 (08 Aug 2022)
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Aristov MM, Moore JW, Berry JF (2021)
Library of 3D visual teaching tools for the chemistry classroom accessible via
Sketchfab and viewable in augmented reality
.
Journal of Chemical Education
98
(
9
):
3032
‑
3037
. https://doi.org/10.1021/acs.jchemed.1c00460
In text citation


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Aristov MM, Geng H, Pavelic A, Berry JF (2022)
A new library of 3D models and problems for teaching crystallographic symmetry
generated through Blender for use with 3D printers or Sketchfab
.
Journal of Applied Crystallography
55
:
172
‑
179
. https://doi.org/10.1107/S1600576721013236
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Berquist RM, Gledhill KM, Peterson MW, Doan AH, Baxter GT, Yopak KE, Kang N,
Walker HJ, Hastings PA, Frank LR (2012)
The digital fish library: using MRI to digitize, database, and document the
morphological diversity of fish.
PloS one
7
(
4
):
e34499
. https://doi.org/10.1371/journal.pone.0034499
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Carnevali L, Ippoliti E, Lanfranchi F, Menconero S, Russo M, Russo V (2018)
Close-range mini-UAVs photogrammetry for architecture survey
.
The International Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences
XLII-2
:
217
‑
224
. https://doi.org/10.5194/isprs-archives-xlii-2-217-2018
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Chenoweth EM, Houston J, Huntington BK, Straley JM (2022)
A virtual necropsy: applications of 3D scanning for marine mammal pathology and
education.
Animals
12
(
4
):
527
. https://doi.org/10.3390/ani12040527
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Colomina I, Molina P (2014)
Unmanned aerial systems for photogrammetry and remote sensing: A review
.
ISPRS Journal of Photogrammetry and Remote Sensing
92
:
79
‑
97
. https://doi.org/10.1016/j.isprsjprs.2014.02.013
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ffish.asia (2022)
Database for freshwater fish (+something) biodiversity of Asia
. https://ffish.asia. Accessed on: 2022-8-02.
In text citation


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floraZia.com (2022)
Database for flora biodiversity of East/SouthEast Asia
. https://floraZia.com. Accessed on: 2022-8-02.
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Honkavaara E, Arbiol R, Markelin L, Martinez L, Cramer M, Bovet S, Chandelier L,
Ilves R, Klonus S, Marshal P, Schläpfer D, Tabor M, Thom C, Veje N (2009)
Digital airborne photogrammetry—a new tool for quantitative remote sensing?—a
state-of-the-art review on radiometric aspects of digital photogrammetric images
.
Remote Sensing
1
(
3
):
577
‑
605
. https://doi.org/10.5167/uzh-24008
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Kano Y, Nakajima J, Yamasaki T, Kitamura J, Tabata R (2018)
Photo images, 3D models and CT scanned data of loaches (Botiidae, Cobitidae and
Nemacheilidae) of Japan.
Biodiversity Data Journal
6
:
e26265
. https://doi.org/10.3897/BDJ.6.e26265
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Kano Y, Kurita Y, Kanno K, Saito K, Hayashi H, Onikura N, Yamasaki T (2019)
Photo images, 3D/CT data and mtDNA of the freshwater mussels (Bivalvia:
Unionidae) in the Kyushu and Ryukyu Islands, Japan, with SEM/EDS analysis of the
shell.
Biodiversity Data ournal
7
:
e32114
. https://doi.org/10.3897/BDJ.7.e32114
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Kano Y (2022a)
Movies of colored 3D models of wild creatures made by photogrammetry
. https://doi.org/10.5281/zenodo.6581034. Accessed on: 2022-8-02.
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Kano Y (2022b)
Colored 3D models of wild creatures made by photogrammetry
. https://doi.org/10.5281/zenodo.6577143. Accessed on: 2022-8-02.
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Nieminski NM, Graham SA (2017)
Modeling stratigraphic architecture using small unmanned aerial vehicles and
photogrammetry: examples from the Miocene East Coast Basin, New Zealand
.
Journal of Sedimentary Research
87
(
2
):
126
‑
132
. https://doi.org/10.2110/jsr.2017.5
In text citation


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Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer M, Packer C, Clune J
(2018)
Automatically identifying, counting, and describing wild animals in camera-trap
images with deep learning
.
PNAS
115
(
25
):
E5716
‑
E5725
. https://doi.org/10.35099/aurora-7
In text citation


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Schambach S, Bag S, Schilling L, Groden C, Brockmann M (2010)
Application of micro-CT in small animal imaging
.
Methods
50
(
1
):
2
‑
13
. https://doi.org/10.1016/j.ymeth.2009.08.007
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Siddiqui SA, Salman A, Malik MI, Shafait F, Mian A, Shortis MR, Harvey ES (2017)
Automatic fish species classification in underwater videos: exploiting
pre-trained deep neural network models to compensate for limited labelled data
.
ICES Journal of Marine Science
75
(
1
):
374
‑
379
. https://doi.org/10.1093/icesjms/fsx109
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Sketchfab (2022)
ffish.asia / floraZia.com (@ffishAsia-and-floraZia) - Sketchfab
. https://sketchfab.com/ffishAsia-and-floraZia. Accessed on: 2022-8-02.
In text citation


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