This call attracted project proposals for academia-industry collaborations to share data, tools, and workflows in the European Open Science Cloud

The Digital Life Science Call for Academia-Industry Collaborations encouraged EOSC-Life partners who wanted to implement a collaborative project together with SME or industry partners to submit project proposals. 

Ten (10) applications were submitted and deemed eligible. These applications first reviewed by the technical experts within the EOSC-Life consortium to assess their technical impact, feasibility, and project maturity, before they were evaluated scientifically. A selection panel of both internal and external experts chose the winning projects from a shortlist of proposals based on transparent selection criteria.

Two (2) projects were selected for funding that proposed to demonstrate how companies can leverage cloud-based bioinformatics pipelines for sustainability and how the further development of analysis software by commercial companies can add value to open research databases:

Funded Project 1

Accessible and scalable detection and identification of foodborne pathogens

Team lead: Bérénice Batut, University of Freiburg

Industry partner: Biolytix AG

Project team: Ralf Seyfarth, Anna Henger, Engy Nasr


Brief project summary:

Metagenomic sequencing has been proposed as a time- and resource effective method for detecting, identifying, and characterising (e.g., resistance or virulence) foodborne pathogens in complex samples in a single sequencing run without prior isolation of the pathogen. The project was designed by the Freiburg Galaxy Team and Biolytix AG to modernise and open up the current detection pipelines using long-read ONT sequencing technologies, developed by Biolytix AG. Based on Galaxy, modern paradigms of open data science were applied to ensure the pipelines are FAIR, accessible, scalable, but also portable, cloud deployable, maintainable, and shareable.

Funded Project 2

QSM4SENIOR: Ultra-high Field MRI Quantitative Susceptibility Mapping reconstruction of the healthy Brain-aging cohort

Team lead: Alexandre Vignaud, CNRS

Industry partner: Ventio

Project team: Miguel Guevara, Jean-Francois Mangin, Yann Cointepas, Ludovic de Rochefort, Michael Bottländer


Brief project summary:

This project will re-use ultra high-field MRI imaging data that has been acquired during a longitudinal study in healthy, aging subjects (the SENIOR study) which has not yet been fully exploited. In particular, the investigation of iron load as a quantitative imaging biomarker for aging and brain disease requires complex image reconstruction techniques. QSM4SENIOR will employ quantitative susceptibility mapping (QSM) using the MEDI software combined to the cloud computing implementation developed by the industry partner Ventio SAS, and FAIRify the imaging data, generating the first quantitative imaging database of highly-resolved 7T MRI data, enhancing the value of the SENIOR database by the addition of a novel imaging biomarker, and targeting interoperability to EOSC-Life cloud research infrastructures.

For more details about the research, read the press release here

Sustainable outcomes:


  • Preliminary results of QSM4SENIOR project (Poster presented at QMR 2022)
    • Introduces a pipeline for computing the QSM based on data from the SENIOR database, allowing the computation of clean and exploitable QSM performed in a secure cloud environment (de.NBI Cloud).
  • Intermediate results of QSM4SENIOR project (Poster presented at ISMRM 2023)
Looking for the Internal Call for Sensitive Data? Find it here.