‘A brief introduction to the use of containers for reproducible image analysis’ with Dr Martin Jones
This webinar was given on 24th February 2026 as part of the CCP-volumeEM Show and Tell Webinar Series.
About this Webinar
Description:
Reproducibility of scientific data analysis is critical, but can be particularly challenging with computational analysis using cutting-edge open-source tools. Installation issues are often encountered, preventing end-users from accessing potentially useful tools for their analysis.
One possible route to improving accessibility and reproducibility of analysis pipelines is the use of so-called “container” systems, such as Docker, Singularity and Apptainer. This introduction aims to demystify what containers are, why they’re useful, and how to use them. While using containers is not a silver bullet to solve all problems, it is a useful addition to a scientist’s toolkit that can assist in the sharing and use of robust analysis software.
Speaker biography:
Martin trained originally as a physicist, with an undergraduate degree in Physics with Electronics and Optoelectronics at Sussex University, followed by a brief foray into the cognitive and computer science department for a masters in Evolutionary and Adaptive Systems. After this, he went back to physics for a DPhil in Experimental Quantum Optics, with applications in quantum computing, also at Sussex. After a couple of years as a physics postdoc in Leeds, he moved to Cancer Research UK to work in the Vascular Biology Lab, initially to build microscopes, but quickly moved largely to computational analysis of biological imaging data. From there, he moved to the CRUK Electron Microscopy team, which then migrated to its current home at the Crick as the electron microscopy science technology platform (EM STP). In this team, he leads the “Microscopy Prototyping” subteam, developing new hardware and software to help deal with volume EM and volume CLEM data, working closely with the software engineering and AI STP at the Crick to ensure software is sustainably developed and is made accessible to end-users.
Intended audience:
Beginner. This basic introduction will be suitable for people who have never used (or even heard of!) containers before and are curious as to whether they might be useful for their analysis.
Learning outcomes:
What containers are
Why they’re useful in scientific analysis
How to get started using containers