M. Benfield, P. Grosjean, P. Culverhouse, X. Irigolen, M. Sieracki et al., RAPID: Research on Automated Plankton Identification, Oceanography, vol.20, issue.2, pp.172-18763, 2007.
DOI : 10.5670/oceanog.2007.63
URL : https://tos.org/oceanography/assets/docs/20-2_benfield.pdf

J. Berges, D. Franklin, and P. Harrison, EVOLUTION OF AN ARTIFICIAL SEAWATER MEDIUM: IMPROVEMENTS IN ENRICHED SEAWATER, ARTIFICIAL WATER OVER THE LAST TWO DECADES, Journal of Phycology, vol.4, issue.6, pp.1138-1145, 2001.
DOI : 10.1111/j.1529-8817.1986.tb00017.x

M. Boland, M. Markey, and R. Murphy, Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images, 3<366::AID-CYTO12>3.0.CO, pp.366-3752, 1998.
DOI : 10.1002/(SICI)1097-0320(19981101)33:3<366::AID-CYTO12>3.0.CO;2-R

P. Bork, C. Bowler, C. De-vargas, G. Gorsky, E. Karsenti et al., Tara oceans. tara Oceans studies plankton at planetary scale. Introduction, Science, vol.348, p.25999501, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01258211

L. Breiman, Random forests DOI: https://doi.org/10, Machine Learning, vol.45, issue.1, pp.5-321010933404324, 1023.
DOI : 10.1023/A:1010933404324

F. Burki, M. Kaplan, D. Tikhonenkov, V. Zlatogursky, B. Minh et al., Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida, Haptophyta and Cryptista, Proceedings of the Royal Society B: Biological Sciences, vol.5, issue.1823, p.26817772, 2016.
DOI : 10.1002/bies.20638

L. Coelho, S. Aabid, and R. Murphy, Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009.
DOI : 10.1109/ISBI.2009.5193098

L. Coelho, Mahotas: Open source software for scriptable computer vision, Journal of Open Research Software, vol.1, 2013.

P. Culverhouse, Human and machine factors in algae monitoring performance, Ecological Informatics, vol.2, issue.4, pp.361-366, 2007.
DOI : 10.1016/j.ecoinf.2007.07.001

C. De-vargas, S. Audic, N. Henry, J. Decelle, F. Mahé et al., Eukaryotic plankton diversity in the sunlit ocean, Science, vol.48, issue.1, pp.1261605-25999516, 2015.
DOI : 10.1111/j.1529-8817.2012.01150.x

J. Del-campo, M. Sieracki, R. Molestina, P. Keeling, R. Massana et al., The others: our biased perspective of eukaryotic genomes, Trends in Ecology & Evolution, vol.29, issue.5, pp.252-259, 2014.
DOI : 10.1016/j.tree.2014.03.006

K. Eliceiri, M. Berthold, I. Goldberg, L. Ibá-n-?-ez, B. Manjunath et al., Biological imaging software tools, Nature Methods, vol.13, issue.7, pp.697-710, 2012.
DOI : 10.1038/ncb0311-183
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659807/pdf

K. Embleton, C. Gibson, and S. Heaney, Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method, Journal of Plankton Research, vol.25, issue.6, pp.669-681, 2003.
DOI : 10.1093/plankt/25.6.669

P. Falkowski and A. Knoll, Evolution of Primary Producers in the Sea, 2007.

B. Hense, P. Gais, U. Jutting, H. Scherb, and K. Rodenacker, Use of fluorescence information for automated phytoplankton investigation by image analysis, Journal of Plankton Research, vol.30, issue.5, pp.587-606, 2008.
DOI : 10.1093/plankt/fbn024

E. Karsenti, S. Acinas, P. Bork, C. Bowler, D. Vargas et al., A Holistic Approach to Marine Eco-Systems Biology, PLoS Biology, vol.6, issue.10, p.22028628, 2011.
DOI : 10.1371/journal.pbio.1001177.g002
URL : https://hal.archives-ouvertes.fr/hal-00691580

J. Kiernan, Formaldehyde, formalin, paraformaldehyde and glutaraldehyde: What they are and what they do, Microscopy Today, vol.8, pp.8-12, 2000.

A. Knoll, Paleobiological Perspectives on Early Eukaryotic Evolution, Cold Spring Harbor Perspectives in Biology, vol.6, issue.1, p.24384569, 2014.
DOI : 10.1101/cshperspect.a016121
URL : http://cshperspectives.cshlp.org/content/6/1/a016121.full.pdf

M. Kolisko, V. Boscaro, F. Burki, D. Lynn, and P. Keeling, Single-cell transcriptomics for microbial eukaryotes, Current Biology, vol.24, issue.22, p.25458215, 2014.
DOI : 10.1016/j.cub.2014.10.026
URL : https://doi.org/10.1016/j.cub.2014.10.026

S. Lin, R. Litaker, and W. Sunda, Phosphorus physiological ecology and molecular mechanisms in marine phytoplankton, Journal of Phycology, vol.43, issue.323, pp.10-36, 2016.
DOI : 10.4319/lo.1998.43.3.0387

N. Macleod, M. Benfield, and P. Culverhouse, Time to automate identification, Nature, vol.247, issue.7312, pp.154-155, 2010.
DOI : 10.1038/467154a

F. Mahe, C. De-vargas, D. Bass, L. Czech, A. Stamatakis et al., Soil protists in three neotropical rainforests are hyperdiverse and dominated by parasites. bioRxiv . DOI: https, 2016.
DOI : 10.1101/050997

D. Marie, F. Partensky, D. Vaulot, and C. Brussaard, Current Protocols in Cytometry, 1999.

D. Marie, F. Rigaut-jalabert, and D. Vaulot, An improved protocol for flow cytometry analysis of phytoplankton cultures and natural samples, Cytometry Part A, vol.63, issue.2, pp.962-968, 2014.
DOI : 10.1007/s10872-007-0008-7
URL : https://hal.archives-ouvertes.fr/hal-01140601

J. Pawlowski, S. Audic, S. Adl, D. Bass, L. Belbahri et al., CBOL Protist Working Group: Barcoding Eukaryotic Richness beyond the Animal, Plant, and Fungal Kingdoms, PLoS Biology, vol.279, issue.(3), p.23139639, 2012.
DOI : 10.1371/journal.pbio.1001419.s002
URL : https://hal.archives-ouvertes.fr/hal-01258240

F. Pedregosa, Scikit-learn: machine learning in python, Journal of Machine Learning Research : JMLR, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. Pepperkok and J. Ellenberg, High-throughput fluorescence microscopy for systems biology, Nature Reviews Molecular Cell Biology, vol.6, issue.9, pp.690-696, 2006.
DOI : 10.1016/j.cell.2006.01.040

S. Pesant, F. Not, M. Picheral, S. Kandels-lewis, L. Bescot et al., Tara Oceans Consortium Coordinators. 2015. Open science resources for the discovery and analysis of Tara Oceans data. Scientific Data 2:150023. DOI: https, p.26029378

K. Rodenacker, B. Hense, U. Jü-tting, and P. Gais, Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation, Microscopy Research and Technique, vol.51, issue.9, pp.708-720, 2006.
DOI : 10.1007/978-3-642-61105-6_39

K. Schulze, U. Tillich, T. Dandekar, and M. Frohme, PlanktoVision ??? an automated analysis system for the identification of phytoplankton, BMC Bioinformatics, vol.14, issue.1, p.23537512, 2013.
DOI : 10.1186/1471-2105-14-115

C. Sieracki, M. Sieracki, and C. Yentsch, An imaging-in-flow system for automated analysis of marine microplankton, Marine Ecology Progress Series, vol.168, pp.285-296, 1998.
DOI : 10.3354/meps168285

H. Sosik and R. Olson, Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry, Limnology and Oceanography: Methods, vol.5, issue.6, pp.204-216, 2007.
DOI : 10.4319/lom.2007.5.204

T. Oceans and C. , Coordinators, Tara Oceans Expedition, Participants Registry of all samples from the tara oceans expedition (2009-2013). PANGAEA. DOI: https, 2017.

E. Williams, J. Moore, S. Li, G. Rustici, A. Tarkowska et al., Image Data Resource: a bioimage data integration and publication platform, Nature Methods, vol.5, issue.8, pp.775-781, 2017.
DOI : 10.1016/j.cub.2016.04.026
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5536224/pdf

M. Zubkov, P. Burkill, and J. Topping, Flow cytometric enumeration of DNA-stained oceanic planktonic protists, Journal of Plankton Research, vol.29, issue.1, pp.79-86, 2007.
DOI : 10.1093/plankt/fbl059