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Machine learning to decipher plankton big data and unravel the link between marine plankton diversity and associated ecosystem services

Objective

"Marine plankton is highly diverse, with millions of taxa and genes. This huge biodiversity plays a key role in providing ecosystem services to human societies, such as oxygen production, carbon export, climate regulation, or food provision. Large datasets of various types have been gathered to document marine plankton, from individuals (e.g. species occurrences, individual images) to communities (acoustic, satellite, metagenomics). Yet, these plankton ""big data"" are largely underexploited, while new tools from machine learning (ML) are now available to mine them. Today, the two main challenges of using ML for plankton big data are: i) to handle, combine, and decipher data of various types, and ii) to use these data to improve marine ecosystem models, especially the ones used by the IPCC and IPBES to anticipate the impact of climate change on our oceans. Hence, there is a crucial need for training a new generation of innovative doctoral researchers (DRs) at the interface of key scientific disciplines such as marine ecology, omics, imaging, satellite, acoustics, numerical modelling, climate sciences and data science, including machine learning. We offer a unique international, multidisciplinary, and multisectoral high quality and innovative research and training network. It gathers European world-class research and training centres, non-governmental organisations, governmental organisations, small and medium enterprises (SMEs), and multinational companies from 6 European countries plus Canada. The non-academic partners bring expertise in plankton harvesting, fisheries management, biodiversity conservation, marine policy, biotechnologies, computer vision, data management, communication, and public engagement. The unique set of skills of our DRs will advance their careers in various sectors such as government and policy, academic research, private consultancy, SMEs or industry, including information and communication technology, and big data analytics."

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2025-DN-01

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Coordinator

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 314 669,16
Address
RUE MICHEL ANGE 3
75794 Paris
France

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Region
Ile-de-France Ile-de-France Paris
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Participants (12)

Partners (11)

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