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arXiv · 2307.03934

Better Research Software Tools to Elevate the Rate of Scientific Discovery -- or why we need to invest in research software engineering

Joran Deschamps·Damian Dalle Nogare·Florian Jug
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Abstract

In the past decade, enormous progress has been made in advancing the state-of-the-art in bioimage analysis - a young computational field that works in close collaboration with the life sciences on the quantitative analysis of scientific image data. In many cases, tremendous effort has been spent to package these new advances into usable software tools and, as a result, users can nowadays routinely apply cutting-edge methods to their analysis problems using software tools such as ilastik [1], cellprofiler [2], Fiji/ImageJ2 [3,4] and its many modern plugins that build on the BigDataViewer ecosystem [5], and many others. Such software tools have now become part of a critical infrastructure for science [6]. Unfortunately, overshadowed by the few exceptions that have had long-lasting impact, many other potentially useful tools fail to find their way into the hands of users. While there are many reasons for this, we believe that at least some of the underlying problems, which we discuss in more detail below, can be mitigated. In this opinion piece, we specifically argue that embedding teams of research software engineers (RSEs) within imaging and image analysis core facilities would be a major step towards sustainable bioimage analysis software.

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Joran Deschamps, Damian Dalle Nogare, Florian Jug. 2023-07-08. Better Research Software Tools to Elevate the Rate of Scientific Discovery -- or why we need to invest in research software engineering. https://arxiv.org/abs/2307.03934

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