Datasets:
Dataset A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5
Plankton and detritus are essential components of the Earths oceans influencing biogeochemical cycles and carbon sequestration. Climate change impacts their composition and marine ecosystems as a whole. To improve our understanding of these changes, standardized observation methods and integrated global datasets are needed to enhance the accuracy of ecological and climate models. Here, we present a global dataset for plankton and detritus obtained by two versions of the Underwater Vision Profiler 5 (UVP5). This release contains the images classified in 33 homogenized categories, as well as the metadata associated with them, reaching 3,114 profiles and ca. 8 million objects acquired between 2008-2018 at global scale. The geographical distribution of the dataset is unbalanced, with the Equatorial region (30° S - 30° N) being the most represented, followed by the high latitudes in the northern hemisphere and lastly the high latitudes in the Southern Hemisphere. Detritus is the most abundant category in terms of concentration (90%) and biovolume (95%), although its classification in different morphotypes is still not well established. Copepoda was the most abundant taxa within the plankton, with Trichodesmium colonies being the second most abundant. The two versions of UVP5 (SD and HD) have different imagers, resulting in a different effective size range to analyse plankton and detritus from the images (HD objects >600 µm, SD objects >1 mm) and morphological properties (grey levels, etc.) presenting similar patterns, although the ranges may differ. A large number of images of plankton and detritus will be collected in the future by the UVP5, and the public availability of this dataset will help it being utilized as a training set for machine learning and being improved by the scientific community. This will reduce uncertainty by classifying previously unclassified objects and expand the classification categories, ultimately enhancing biodiversity quantification.
- Original dataset available online at: https://www.seanoe.org/data/00964/107583/.
- Original dataset license: <cc-by-nc-4.0>.
Details
- train split means (RGB): [0.9602815321070455, 0.9602815321070455, 0.9602815321070455]
- train split standard deviations (RGB): [0.17934853999942507, 0.17934853999942507, 0.17934853999942507]
Samples per class for split train
0: Acantharea 12060.00
1: Acrocirridae 118.00
2: Actinopterygii 34.00
3: Aegina 3.00
4: Aegina citrea 13.00
5: Aequorea 2.00
6: Aequoreidae 15.00
7: Aglantha 2.00
8: Aglantha digitale 100.00
9: Alciopidae 74.00
10: Amphipoda 433.00
11: Annelida 2190.00
12: Anthoathecata 32.00
13: Aphanizomenon 689.00
14: Appendicularia 564.00
15: Atolla 1.00
16: Aulacantha 18245.00
17: Aulacanthidae 232.00
18: Aulatractus 217.00
19: Aulographis 816.00
20: Aulosphaeridae 27307.00
21: Bassia bassensis 9.00
22: Beroe 180.00
23: Beroida 36.00
24: Botrynema brucei 8.00
25: Bythotiaridae 6.00
26: Cannosphaeridae 1409.00
27: Castanellidae 2190.00
28: Cavoliniidae 17.00
29: Cephalopoda 48.00
30: Ceriantharia 74.00
31: Cestidae 64.00
32: Chaeteessa 1.00
33: Chaetognatha 6377.00
34: Circoporidae 259.00
35: Cladocera 13.00
36: Cnidaria<Hydrozoa 4.00
37: Cnidaria<Metazoa 1747.00
38: Coelodendridae 1.00
39: Coelodendrum 270.00
40: Coelographis 2452.00
41: Collodaria 1.00
42: Copepoda ▇ 93218.00
43: Copepoda X 35.00
44: Coronatae 6.00
45: Coscinodiscus 23.00
46: Creseidae 120.00
47: Crustacea 4305.00
48: Ctenocalanus 1.00
49: Ctenophora 2170.00
50: Ctenophora XX 4.00
51: Cubozoa 1.00
52: Cytocladus 8.00
53: Decapoda 22.00
54: Diatoma 27876.00
55: Dinophyceae 164.00
56: Dolichospermum 179.00
57: Doliolida 1275.00
58: Echidnophaga 1.00
59: Echinodermata 114.00
60: Enteropneusta 124.00
61: Eucalanidae 664.00
62: Eukaryota 1.00
63: Eukrohnia 7.00
64: Eumalacostraca 9233.00
65: Euphausiacea 531.00
66: Eurypharynx pelecanoides 1.00
67: Flota 179.00
68: Flota sp. 9.00
69: Foraminifera 2959.00
70: Fritillaria 13.00
71: Galatheidae 4.00
72: Gnathostomata 431.00
73: Gymnosomata 87.00
74: Halicreas 5.00
75: Halicreas minimum 17.00
76: Halicreatidae 184.00
77: Harpacticoida 65.00
78: Helix aspersa 2.00
79: Hydrozoa 8167.00
80: Leptothecata 96.00
81: Limacina 1.00
82: Limacina helicina 1.00
83: Limacinidae 1015.00
84: Lobata 111.00
85: Luciferidae 1.00
86: Medusettidae 450.00
87: Melosira 24.00
88: Metazoa 28.00
89: Mollusca 739.00
90: Munnopsinae 1.00
91: Munnopsis 57.00
92: Narcomedusae 917.00
93: Noctiluca sp. 1.00
94: Nodularia 501.00
95: Nudibranchia 1.00
96: Oithona 1.00
97: Olindiidae 1.00
98: Orodaria 23.00
99: Ostracoda 8407.00
100: Phaeodaria 4.00
101: Phyllodocida 23.00
102: Phyllodocidae 30.00
103: Platyhelminthes 2.00
104: Pleuroncodes 258.00
105: Poeobius 355.00
106: Pseudocalanus 99.00
107: Pterotracheoidea 1.00
108: Pyrosoma 333.00
109: Pyrosomatida 1.00
110: Rhizaria 10.00
111: Rhizostomeae 1.00
112: Rhopalonema 5.00
113: Rhopalonema velatum 2.00
114: Rhopalonematidae 5.00
115: Salpida 384.00
116: Salpidae 1.00
117: Sarsia 16.00
118: Scyphozoa 253.00
119: Semaeostomeae 1.00
120: Siphodera 2.00
121: Siphonophorae 2079.00
122: Sminthea 3.00
123: Solmaris 1151.00
124: Solmarisidae 6.00
125: Solmundella bitentaculata 168.00
126: Stegocephalus inflatus 1.00
127: Swima 21.00
128: Teleostei 1.00
129: Thaliacea 44.00
130: Thecosomata 193.00
131: Tomopteridae 13.00
132: Tomopteris 201.00
133: Trachymedusae 129.00
134: Trichodesmium ▇ 97516.00
135: Trochophora 7.00
136: Tunicata 2.00
137: Tuscaroridae 85.00
138: Zoantharia 2.00
139: actinula 17.00
140: artefact 49467.00
141: badfocus<Copepoda 5.00
142: badfocus<artefact ▇▇▇▇▇▇▇▇▇▇▇ 1157369.00
143: ball with spines 663.00
144: beanlike 1529.00
145: body 493.00
146: bubble 8324.00
147: caps with filament 2.00
148: chain 1.00
149: colonial<Aulosphaeridae 821.00
150: colonial<Cannosphaeridae 80.00
151: colonial<Collodaria 2252.00
152: compact ▇▇▇ 346629.00
153: copepoda eggs 22.00
154: dark<detritus 31239.00
155: dark<fluffy ▇▇▇▇▇▇ 605750.00
156: dark<sphere 16676.00
157: darkrods 14.00
158: detritus ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 3591346.00
159: double 9.00
160: double sphere 557.00
161: double spike 16.00
162: duplicate 5228.00
163: egg 53.00
164: egg 1 temp 5.00
165: egg unkn temp<Engraulidae temp 1053.00
166: egg unkn temp<Sardina temp 30.00
167: elongated star with ball 55.00
168: elongated stick 19.00
169: fecal pellets 4.00
170: feces ▇▇ 168435.00
171: fiber ▇▇▇▇ 435864.00
172: filament 29.00
173: fluffy<detritus 38279.00
174: fluffy<fiber 9835.00
175: gelatinous 1.00
176: hairy capsule 133.00
177: head<Chaetognatha 62.00
178: head<Crustacea 5.00
179: helix 20.00
180: house 7912.00
181: juvenile 192.00
182: larvae<Annelida 3.00
183: larvae<Crustacea 58.00
184: leg<Coelodendridae 1493.00
185: leg<Crustacea 3.00
186: light aggregates 1.00
187: light<detritus 17210.00
188: light<fluffy ▇▇▇ 302625.00
189: light<sphere 45.00
190: like<Acantharea 228.00
191: like<Copepoda ▇ 70229.00
192: like<Limacinidae 57.00
193: like<Ostracoda 11.00
194: like<Phaeodaria 1.00
195: like<Salpida 11.00
196: like<Temoridae 280.00
197: like<Trichodesmium 1432.00
198: like<colonial 1079.00
199: like<egg 127.00
200: living 16.00
201: megalopa 1.00
202: molt 236.00
203: multiple organisms 4.00
204: nauplii 5.00
205: nucleus with four arms 24.00
206: other<living 4033.00
207: other<plastic 39.00
208: othertocheck 13825.00
209: ovigerous 117.00
210: ovoid 8451.00
211: part<Cnidaria 55.00
212: part<Crustacea 31.00
213: part<Siphonophorae 380.00
214: part<other 1710.00
215: pluteus 13.00
216: protist with spike 5.00
217: puff 41936.00
218: rhizaria like 7733.00
219: rhizaria temporary 29612.00
220: side 3484.00
221: solitaryblack 5767.00
222: solitaryglobule 1718.00
223: sphere<detritus 10.00
224: sphere<othertocheck 4.00
225: spiky caps 312.00
226: star with ball 204.00
227: t001 344.00
228: t002 816.00
229: t003 109.00
230: t004 147.00
231: t006 77.00
232: t007 279.00
233: t010 5.00
234: t011 342.00
235: t013 808.00
236: t018 2.00
237: t019 13.00
238: tail<Appendicularia 1.00
239: tail<Chaetognatha 64.00
240: tail<Crustacea 122.00
241: temp cross 88.00
242: tentacle<Cnidaria 6503.00
243: tentacle<Ctenophora 64.00
244: tentacle<Siphonophorae 1381.00
245: tentacle<gelatinous 3.00
246: top-bottom 267.00
247: tornaria larvae 1.00
248: trocophora 12.00
249: tuff 46740.00
250: turbid 9507.00
251: unknown 2265.00
252: veliger 3083.00
253: with-eggs 9.00
Reference
Nocera Ariadna, Stemmann Lars, Babin Marcel, Biard Tristan, Coustenoble Julie, Carlotti François, Coppola Laurent, Courchet Lucas, Drago Laetitia, Elineau Amanda, Guidi Lionel, Hauss Helena, Jalabert Laëtitia, Karp-Boss Lee, Kiko Rainer, Laget Marion, Lombard Fabien, McDonnell Andrew, Merland Camille, Motreuil Solène, Panaïotis Thelma, Picheral Marc, Rogge Andreas, Waite Anya, Irisson Jean-Olivier (2025). A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5. SEANOE. https://doi.org/10.17882/107583
BibTEX
@article{dataset:globaluvp5net,
title = {A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5},
author = {Nocera Ariadna and Stemmann Lars and Babin Marcel and Biard Tristan
and Coustenoble Julie and Carlotti François and Coppola Laurent
and Courchet Lucas and Drago Laetitia and Elineau Amanda
and Guidi Lionel and Hauss Helena and Jalabert Laëtitia
and Karp-Boss Lee and Kiko Rainer and Laget Marion
and Lombard Fabien and McDonnell Andrew and Merland Camille
and Motreuil Solène and Panaïotis Thelma and Picheral Marc
and Rogge Andreas and Waite Anya and Irisson Jean-Olivier},
year = 2025,
journal = {SEANOE},
doi = {10.17882/107583},
affiliation = {Centro para el Estudio de Sistemas Marinos, CCT-CENPAT-CONICET, U9120ACD, Puerto Madryn, Chubut, Argentina.
Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV, F-06230 Villefranche-sur-Mer, France.
Département de Biologie, Université Laval, Québec, Canada.
Laboratoire d'Océanologie et de Géosciences (LOG), Université du Littoral Côte d'Opale, Université Lille, CNRS, IRD, UMR 8187, Wimereux, France.
Mediterranean Institute of Oceanography, Aix-Marseille Université, Université de Toulon, CNRS, IRD, UMR 7294, Marseille, France.
Sorbonne Université, CNRS, OSU STAMAR, UAR2017, 4 Place Jussieu, 75252 Paris CEDEX 05, France.
Sorbonne Université, CNRS, Institut de la Mer de Villefranche, IMEV, F-06230 Villefranche-sur-Mer, France.
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany.
NORCE Norwegian Research Centre, Bergen, Norway.
School of Marine Sciences, University of Maine, Orono, ME, USA.
College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, Alaska, USA.
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany.
Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada.}
}
Usage
from datasets import load_dataset
dataset = load_dataset("project-oceania/globaluvp5net")
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