Imaging is the interdisciplinary discipline par excellence. From sample preparation to optical design and image processing, imaging workflows nowadays require the convergence of various skills and expertise. It has become increasingly difficult–if not impossible–for a single group to produce, process, and analyze the data all by itself. Hence, the impact of research achievements in imaging depends on the success of interdisciplinary collaborations and, by extension, on the efficient sharing of expertise and resources (data, metadata, code). This is a challenging endeavour as common practices and needs in imaging vary greatly among disciplines and peers.
Although there is probably no universal solution to this conundrum, we strongly believe that the removal of technical barriers is a key first step toward a more collaboration-prone environment for EPFL’s imaging community. In that optic, three imaging-related proposals were made to EPFL Open Science Fund in 2019; all three were accepted and are presented below.
Imaging-Related Open Science Projects
The campus-wide initiative [email protected] and the Swiss Data Science Center (SDSC) join forces to develop a strategy to facilitate Interdisciplinary collaborations in imaging at EPFL, and beyond. Using the functionalities of the open-source platform RENKU, data, metadata and code originating from the imaging community at EPFL will be made available openly to the global imaging community as a repertoire of reproducible, reusable, and well-documented image-processing workflows.
Modern light-microscopy techniques generate tremendous amounts of images and movies that need careful attention to be useful. Like any other digital data, they benefit from following the FAIR principles. The Bioimaging and Optics platform (BIOP) is deploying the open source user-friendly image database software OMERO provided by the Open Microscopy Environment. On top of this infrastructure, it develops a connector that allows researchers at EPFL and beyond for adopting rapid and reproducible image processing with neural networks.
Three EPFL laboratories led by Aleksandra Radenovic (LBEN), Suliana Manley (LEB) and Daniel Sage (LIB) are joining forces to develop a comprehensive platform for researchers in the field of super-resolution localisation microscopy (SMLM). This community-led initiative (SMLM HUB) will provide benchmarking resources, including reference datasets, scientific evaluation of the image reconstruction process, automatic assessment tools, interactive data exploration, and deep learning models. It will promote transparency and reproducibility of complex procedures both for the end-users and for engineers developing new methods.