Code Work in Science: How it changes, and Why it matters how we talk about change

As software projects in science become more ambitious (and expensive in time, energy, and money), they increasingly require a language that recognizes and rewards the collective pursuit of uncertain possible worlds.

I conducted an 18-month interview and observation study of code work in science. The implication of my findings for design and practice is to articulate goals in ways that can reward community and skill related progress, because there is relatively less uncertainty associated with these than with increasingly large or ambitious programming projects. The take-aways are aimed at anyone about to invest time and energy in a project that carries with it an inevitable amount of risk:

  • When building something integrative or holistic, consider ways that its components can be made available piece-wise and engage with the existing skill and enthusiasm in the target-user scientific community.
  • Focusing on technological deliverables and goals creates pressure; focusing on time spent cultivating community or learning a new vocabulary or way of thinking creates momentum.
  • Set initial, ambitious goals, against which outcomes will be measured, without unintentionally privileging any one part (technological, social, or cognitive) of the working environment over the others.
Figure 1.1: The proposed conceptual framework, showing influences that the working environment and the collective imagination of the perfect world exert and are subject to, relative to moments of flux, that time when the change occurs.

When I talk about “code work,” I include scripting, software engineering, using command-line interfaces, or typesetting your manuscript in LaTeX. Based on the feedback to this work so far, I believe my findings apply to many domains that experimenting with code. However, my study had to do with code work in oceanography. The study of the ocean draws people from many disciplines, and so there are many different complementary methods, all of which can and do engage differently with code work.

Figure 2.1: The place of code work in oceanography. Loci for code work include: (A) data processing tightly coupled to collection method (with and without code work) (B) data presentation and stewardship; (C) existing modeling practice; (D) an opportunity for innovation for better comparisons. In this diagram, I distinguish observational and modeling oceanography. Additionally, the dashed border to denote ‘optional’ code work indicates code work loci with particularly wide ranges of automation, from end-user programming (with Excel or LabVIEW), to scripting (with R or Python), to engineering a custom analytic pipeline.
Figure 7.1: Three different ways in which computer science intervenes in domain science code work. (a) By working on a particular project that is part of a larger infrastructural and collaborative effort and which ultimately aims to support many scientists in their study of many different phenomena; (b) by organizing educational interventions, like workshops, tutorials, and mentorship programs to support scientists who code in developing programming skills, who are then mostly on their own to apply those skills to particular projects and research questions of interest; and (c) by collaborating closely with a group pursuing particular research questions, and building tools or intervention specifically tailored to that group or context. “CS/SE” stands for “Computer Science / Software Engineering” but extends other computing-affiliated researchers.

The setting of my qualitative study is “oceanography” intersecting in some way with “code work.” The population consists of four teams in the Pacific Nrothwest, as well as natural scientists who had attended Software Carpentry workshops or similar interventions intended to overview computing skills to scientists in a few days.

Figure 3.1: Study participation summary, grouped by inclusion criterion.
Figure 3.2: Summary of the different group events included in they study, distinguished by interactivity and specificity of work. Interactivity refers to the extent to which multiple people are actively working together on the same piece of code. Specificity refers to how explicit, articulated, and/or pre-determined the aim of the event is. Excluded from this chart is “shadowing” which focuses on the individual, though may involve also participating in some of these events.

I interviewed people individually and observed group events, illustrated to the left. Over 18 months, I conducted about 300 hours of observation, and collected several dozen interviews. The use of qualitative methods alone is not groundbreaking in computer science; I review excellent work in software engineering and computer supported cooperative work (CSCW) that make use of qualitative methods. However, this work is unique in its simultaneous recognition of the idealized software engineering concepts (“etic”) at the same time as aiming to contextualize and describe the participant’s own meanings and values on the relevant subjects (“emic”).

2 concepts form the foundation of this conceptual framework: the working environment and the perfect world. The working environment is a highly personalized set of social, cognitive, and technical resources at an individual’s disposal. “Social” ones include colleagues, supervisors, mentors, and office-mates. “Cognitive” ones include, for example, being very good at interpreting a specialized visualization that most people outside your field would take a long time to understand. “Technical” resources comprise all those things that must be downloaded, installed, and otherwise “set up” for you to be productive.

Figure 4.1: Desirable qualities of code in the perfect world, as they influence audience access. In the perfect world, denoted in this illustration with a star, code has the overlapping qualities of being usable, extensible, and understandable by everyone. Something can be “understandable” without being usable or extensible if it serves to document or guide. For example, code on StackOverflow sometimes cannot be run, but is still “understandable” enough to be useful. Code cannot be both “usable” (in an end-user sense) and “extensible” (by a motivated coder) without also being understandable. Scientific code typically resides at the top of this chart: it can be made to have any of the properties, but only to motivated uses through extensive, contextualized guidance. Moving downward in this illustration toward this star expands the potential audience, reduces the amount of “hand-holding”, and comes closer to the perfect world.

Think of the perfect world as that world if you would build if only you had the time. Or the world you would have built if only you knew in the past what you know know. Crucially, having a concept of a perfect world is not itself reason enough to build anew.

The perfect world is collectively-imagined, socially-negotiated, and envisioned with respect to an audience. Where the working environment is an individual-centric way to understand how code work is done, the perfect world is a more socially-situated concept.

The working environment is subject to change, and the direction of this change is informed by a collective vision of the perfect world. When I talk about deliberate change, I am talking about the myriad small decisions to try something new or go the familiar route. Together, these may accumulate to the kind of slow drift that is only visible in retrospect. These small moments of flux, on the other hand, are visible, but quickly forgotten, because we lack ways to constructively articulate the decision-making work that they require. However, they are important, so here is a set of concepts to talk about them.

Figure 6.1: The cycle of deliberate change, zoomed in on the moment of flux. Building on Figure 1.1 by breaking the “moment of flux” into its components. The flow from awareness, to intention, and finally to action is driven by momentum and opportunity, which can be (often is) external to the individual or project. The flow can also go in the reverse, “de-escalation” direction.

Here’s an example of using this vocabulary to actively listen to a study participant describing deliberate change in code work practice:

I’ve been doing increasing amounts of data processing, starting during my PhD with a lot of my own data, using some larger online databases. I am planning on starting doing more of that (1). I guess there’s two things, one is that often we’re just learning on our own, and that’s effective to a certain extent but if you ever want to try something new, there’s a lot of inertia for trying something totally new (2), so it’s nice to get an introduction into whatever’s going to be new to you, to give you that boost so that you can actually not be afraid to try it. In my case, it was GitHub that I’d heard a lot about (3), didn’t really quite understand how it worked or what its purpose was, so I wanted to learn about that. The second thing is that I’m starting a lab of my own as a professor in the fall. And with my own students and postdocs coming in, I want us to do things like GitHub and version control. [I want to learn these skills myself] so that I can best teach them and establish good practices within my own lab (4).

This person had been “doing an increasing amount of data processing” (programmatically, involving code work) and is “planning on starting doing more” (1). This pertains to an anticipation of the near-or-far future, and is an example of future-oriented motivation for programming skill expansion or acquisition. Points (2–4) exemplify typical sentiment toward GitHub as an instantiation of a desirable best practice. Because the speaker places trust in the social context (3), it is not necessary to “quite understand how it worked or what its purpose was” in order to attend a workshop. Attending the workshop was itself the act of change deliberately undertaken.

How long is it necessary to put time and energy into something before giving up on it? The kind of of change that Chapter 6 focuses on requires a step into the unknown.
Figure 6.2: Illustration of incremental change as containing uncertainty, which plays a crucial role in the moment of flux. Although the researcher may have had prior experience with addressing each of the following challenges, they still have few means to estimate when they will come up: (a) integration challenges that demand changing another component first; (b) verification and validation of the overall pipeline post-change; (c) discovery of a preferable alternative; (d) finding out that the intervention does not solve the problem. The figure distinguishes steps that are relatively approachable and map onto prior experience (solid line) from those that are filled with uncertainty in terms of time and commitment (dashed line).

As software projects in scientific contexts involve more people, longer time spans, and more ambitious collaboration between disciplines, understanding how coding practices influence scientific inquiry is increasingly important. The discussion of “best practices” in open science encourages the sharing of negative results and disappointing data as a top priority. This call for reflection on failure must be extended to include code work. With data as well as with code sharing, repeated “best practices” are not sufficient to inspire change, even for those scientists who openly feel they “should” do it. The conceptual framework I propose creates optimistic vocabulary for reflecting upon deliberate changes, big and small.

This is the extra-short, figures-only version of my recently-completed dissertation, which you can get in full [PDF] here. If you only read one chapter, read Chapter 7. It’s full of ethnographic vignettes and provides additional detail and context for the take-aways listed in the beginning of this post. Feedback happily welcome!