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How can we make AFM data analysis more open and reproducible? Making science more open and reproducible is increasingly a priority for government1, funding bodies and journals. It’s also a way of making research outputs including data, software and publications more impactful and accessible. Many of the practical steps needed to make research open and reproducible also make analysis workflows more effective and reduce the need for rework.
In this presentation, we will explore what reproducibility and open science mean in general - how it can help to provide data and analysis code when publishing2 and to make data Findable, Accessible, Interoperable and Reusable (FAIR)3. We will look at how these ideas can be applied to AFM data analysis and identify domain specific problems. We will reflect on how we can learn from other areas (e.g. bioinformatics, epidemiology), and where AFM is relatively unique. It will be an opportunity to take a pulse-check on the field and ask ourselves if we agree that there is a “reproducibility crisis”4. Practical steps to improve reproducibility and openness will be presented covering data management, infrastructure and research software. I’ll focus on aspects of research software engineering such as version control, notebooks, documentation, collaborative coding and testing. We will explore how researchers can collaborate with other professionals to move forwards on this without compromising productivity - spending less time on technicalities of finding data and executing analysis and more time on world-class research.
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