Version Control & Team Science Fundamentals
[Overview Under Construction]
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Introduction to Version Control
LTER Workshop Materials
The workshop materials we will be working through live here but for convenience we have also embedded the workshop directly into the SSECR course website (see below).
Our specific plan is to cover:
- Version Control Fundamentals
- Using GitHub with a Browser
- Using GitHub with an IDE
- Recommendations for Collaborating with Git/GitHub
Science of Team Science
Research in management, organizational behavior, and psychology has long focused on the performance of teams–often in military, healthcare and industrial contexts. While many aspects of this work are also relevant to scientific teams, there are some key differences having to do with differences in context, leadership, and incentives. In the early 2000’s–as collaboration in science increased–the need for empirical research into the workings of science teams became apparent. The field of “science of team science” or SiTS was launched in 2006 with a conference at the National Institutes of Health (Stokols et al. 2008).
A National Academies study on the Science of Team Science (NRC 2015) assembled the existing evidence base and launched a flurry of research into how team composition, coordination, support, and organizational context could improve outcomes for science teams. A new National Academies study on Research and Application in Team Science is currently in progress. Our goal here is not to review the whole field, but to provide a framework for thinking about the team functioning and process and to identify some key team science practices that are supported by both research and practical experience.
Predictable Team Trajectory
Creating a team is not just a matter of putting a bunch of people in a room together. Social scientists have identified consistent patterns in the evolution of teams (Tuckman 1965, Tuckman and Jenson 1977). Knowing that this is a process nearly every team experiences may make it (at least somewhat) more comfortable.
Teams that are assembled from across organizations must agree to adopt a common set of norms and processes in order to progress from storming to performing. This can feel like a detour from the science, but a modest early investment in developing shared practices pays off in the long run.
Instrumental Benefits of Diverse Teams
There is pretty good evidence that collaborative teams produce research that is more novel and has higher impact than work produced by individuals or smaller more homogeneous groups (Lee at al. 2015, Hong and Page 2024). Woolley et al (2010) found evidence for a “collective intelligence” in teams, which is not strongly correlated with the average or maximum individual intelligence of group members but is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group.
Similarly, in a study of 6.6 million medical research papers, Yang et al. found that mixed gender teams consistently produced more novel and more impactful products. In another bibliographic analysis Abbasi and Jaafari (2013) found that inter-institute and inter-university collaborations resulted in higher-impact publications. Interestingly, the result was much weaker for international collaborations.
It seems reasonable to expect that the effects of cultural and economic diversity on teams would be similar to that of gender diversity, but those factors remain harder to parse at this scale. In any case, the bump in creativity or publishing impact is only a happy side effect of assembling a diverse team. The real reason to do so is that it allows us to tackle bigger questions, makes our findings more relevant, our science more fun, and our world more fair. What it does not do (at least in our experience) is make the process faster!
A More Nuanced View Emerges
The paradox of team science is that the very factors that slow progress may be exactly the factors that generate new insight – Milliken and Martins’ (1996) double-edged sword. The pressing question becomes not: “Does diversity improve team performance?” but rather: “How and when does diversity improve team performance?”
What Mechanisms are Responsible for the Diversity Effect?
Information Elaboration
The categorization-elaboration model (CEM, van Knippenberg et al. 2004) proposed that information elaboration—-that is, the exchange, discussion, and integration of task-relevant information and perspectives, was responsible for many of the benefits attributed to diverse groups. But later researchers found there were a few necessary conditions for cognitive elaboration to take place and for groups to reap the benefits. Only when team members brought a learning goal orientation to their work and when they remained open to revising their original ideas (Nederveen Pieterse 2013) did diversity improve team performance.
Avoiding ‘groupthink’
We are all familiar with the “we’ve always done it this way” effect that can happen when a group of people have been working together for a while. By introducing people from new fields, laboratories, or cultures, that complacent thinking is disrupted. Often, the very act of justifying why we do something the way we do can invite a rethinking and improvement.
Metacognition
Metacognition, or “thinking about thinking” requires individuals to reflect and articulate their process for achieving new knowledge. What information goes in? Is information missing? How should it be analyzed and interpreted? Are those conclusions justified?
Enhanced group scanning ability and consideration of alternative solutions
A science team may include members from different research disciplines, sectors, geographies or cultures. Along each of those axes, team members will have different personal networks and be more (or less) familiar with different literatures, models, communities, tools, and solutions. Collectively, the group has a much broader range of information to draw on…but only if group members feel empowered to contribute.
Better task completion and more efficient use of resources
“Many hands make light work” the saying goes. Think of a meta-analysis where 10 group members can each read 30 papers instead of 1 individual reading 300 papers. Dividing the workload can speed up the process, but only if there is an efficient way to manage dividing the work and then bringing the results back together again. Similarly, relying on a few skilled coders can be much more efficient than each individual writing their own code, but unless the group has a mechanism for getting broad input on key decisions, they will lose the value created by bringing together a larger group.