title: Scientists as Software Users and Developers date: 2019-07-12
The scientific Python ecosystem is built by and for people who fall along a continuum between software user and software developer, and this motivates technology choices that may at first seem puzzling from the outside.
In many industries, software developers and users are clustered into distinct roles, producer and consumer. The developer’s mandate is to provide an end-product that meets the users’ needs. Software in science is different. There are scientists who use software as is, never needing to open the hood or form a clear mental model of how it works internally. There are also Research Software Engineers who spend 100% of their time writing code for scientific applications. But there are many scientists situated along a continuum between these two.
The choice of technologies reflects that continuum. In many industries, a developer writing a web application might choose a popular backend framework like Flask or Django and a popular frontend like React or Vue. There is a large community behind those projects, and it is comparatively easy to hire developers who can work on them.
Within the SciPy community, a scientist/developer might choose to implement their web application as a Jupyter notebook rendered as a web application using Voila. This might strike an outsider to that community as an odd choice, as it adds some layers of complexity and relies on tools that are, in the wider world, less established. Why do scientists do this?
The same tool may be picked up by a scientist who never looks under the hood of the software they use; by another scientists who enjoys writing detector drivers in C on the weekend; and by a graduate student who starts at one extreme and evolves over time toward the other. These tools can satisfy the needs of all those scientists—with the same codebase.