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IMAGINE

Next generation imaging for biology across scales

IMAGINE will provide technologies to probe structure and function of biological specimens in their natural context

Where are we at?

IMAGINE passed the first reporting period with “Satisfactory” observation from the European Commission. On May 2024, the second period has begun and the Technology Validation WPs have been officially started.

Fully commit to the FAIR principles, IMAGINE shares and updates its outcomes (deliverables, publication and other documents) through this page.

Deliverables

The purpose of this deliverable is to present a basic overview of the project management of the IMAGINE project that will be implemented across the consortium members. The project management standards were created in order to form a transparent, efficient, and effective coordination across all work packages.


The IMAGINE project DMP has been developed by Work Package 13 on Project Management and Outreach, which is an integral part of the governance structure, and oversees the quality assurance of the project outcomes. This is version 0.1 of the IMAGINE Data Management Plan (DMP) has been developed in consultation with Work Package 6 on AI-based image analysis across scales, and is delivered in month 6 of the project. It encompasses how IMAGINE will organise, manage, distribute and disseminate the data that are generated and used during the project. This project DMP follows the European Commission’s Horizon Europe Data Management Template version 1.0 (published on 5 May 2021). It is a live document that will be reviewed and updated periodically during the project to ensure it remains up to date.


The purpose of this deliverable is to present the implementation of publishing the new official homepage of IMAGINE project and to introduce the dissemination plan through the homepage. This deliverable focuses on brief information about the general structure of the homepage and the communication plan for the homepage.


This IMAGINE project deliverable has been developed by Work Package 13 on Project Management and Outreach, which is an integral part of the governance structure, and oversees the quality assurance of the project outcomes. Together with D13.1 Project Management, D13.2. Data Management Plan, and D13.3 IMAGINE Homepage, this deliverable encompasses how IMAGINE outcome will be shared, published, and targeted to IMAGINE’s stakeholders. This deliverable was written following the Annotated Grant Agreement by the European Commission dated 01 April 2023. As a mandatory deliverable, IMAGINE covers all plans including the activities that have been executed after the start of the project.


This deliverable is an obligatory deliverable following the Annotated Grant Agreement by the European Commission dated 01 April 2023. This deliverable will be updated every reporting period.

Publication

Félix, R; Paleček, D; Correia, T. Colour science with lasers, gummy bears, and rainbows. Issue 66. Science in School: The European Journal for Science Teachers. https://www.scienceinschool.org/wp-content/uploads/2024/01/Issue-66-Gummy-Bears.pdf

Gemin, O., Armijo, V., Hons, M., Bissardon, C., Linares, R., Bowler, M. W., … & Papp, G. (2024). EasyGrid: A versatile platform for automated cryo-EM sample preparation and quality control. bioRxiv, 2024-01. https://doi.org/10.1101/2024.01.18.576170

Albers, J., Nikolova, M., Svetlove, A., Darif, N., Lawson, M. J., Schneider, T. R., … & Duke, E. (2024). High throughput tomography (HiTT) on EMBL beamline P14 on PETRA III. Journal of Synchrotron Radiation31(1). https://doi.org/10.1107/S160057752300944X

Berger, C., Dumoux, M., Glen, T. et al. Plasma FIB milling for the determination of structures in situ. Nat Commun 14, 629 (2023). https://doi.org/10.1038/s41467-023-36372-9

Dumoux, M; Glen, T; Smith, J; Ho, E; Perdigão, L; Pennington, A; Klumpe, S; Yee, N; Farmer, D; Lai, P; Bowles, W; Kelley, R; Plitzko, J; Wu, L; Basham, M; Clare, D; Siebert, C; Darrow, M; Naismith, J; Grange, M (2023). Cryo-plasma FIB/SEM volume imaging of biological specimens. eLife 12:e83623. https://doi.org/10.7554/eLife.83623

Obando, M., Bassi, A., Ducros, N. et al. Model-based deep learning framework for accelerated optical projection tomography. Sci Rep 13, 21735 (2023). https://doi.org/10.1038/s41598-023-47650-3

Obando, M; Bassi, A; Ducros, N; et al. ToMoDL: A model-based deep learning framework for optical projection tomography, 07 September 2023, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-3318045/v1

Schiøtz, O.H., Kaiser, C.J.O., Klumpe, S. et al. Serial Lift-Out: sampling the molecular anatomy of whole organisms. Nat Methods (2023). https://doi.org/10.1038/s41592-023-02113-5

Others

Brand Guidelines

Introductory Poster


Contact us

This project is funded by the European Union (GA#101094250). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. 

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