Abstract
This article examines the role of visualisations in astrophysics programming work, showing that visualisations are not only outputs for those producing them, but can help those developing them understand how to do their work. Studies of visualization in programming have mainly been of social and cultural factors influencing scientific research. We concentrate on the material aspects of scientific work, as of interest in their own right and on methodological grounds (since capturing the material practices of computer screen-work is an underexplored area). Using a ‘video-aided ethnographic’ method we analyse an episode of computational astrophysics involving the use of the Python programming language. We identify a selection of activities comprising the screen work of an astrophysics researcher to unpack how those activities contribute to the production of scientific knowledge.
Highlights
Science and programmingAs computer tools have become increasingly prominent in routine scientific work, so they have become increasingly pertinent to social studies of science, which focus on the constructing and constraining functions of interaction in an era of computational and digital science
We examine one astrophysics researcher’s activities over one working day, as he attends to a problem in developing a program for automatically identifying instances of the astronomical phenomenon of gravitational lensing
The researcher in question (HR) was a postgraduate student, with an undergraduate degree in physics incorporating the learning of programming languages in addition to classes in more conceptual topics
Summary
Science and programmingAs computer tools have become increasingly prominent in routine scientific work, so they have become increasingly pertinent to social studies of science, which focus on the constructing and constraining functions of interaction in an era of computational and digital science. Agar, 2006; Bijker et al, 2016; Bruun and Sierla, 2008; Götschel, 2011; Hine, 2006; Larivière et al, 2016; Louvel, 2012; Pettersson, 2011; Mulinari et al, 2015; Rall, 2006; Sundberg, 2010; Voskuhl, 2004) present computer-aided scientific projects as comprising distinct expertises: the practical hands-on skills of programmers and the conceptual/theoretical knowledge of the scientist, combined through collaboration. Hine argues that: This division of labour [between science/ knowledge and computing/programming] is conventional in [the] development of information systems. The database developer is responsible for identifying ‘user requirements’, and is expected to get to know users and find out what their needs are. (Hine, 2006: 281)
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