Scatterplots are used in many disciplines, which makes them useful for communicating across disciplines. They are also common in newspapers, online media and elsewhere as a tool to communicate research results to stakeholders, ranging from policy makers to the general public. What makes a good scatterplot? Why do scatterplots work? What do you need to watch out for in using scatterplots to communicate across disciplines and to stakeholders?
Why should transdisciplinarians, in particular, care about multilingualism and what can be done to embrace it?
From a linguist’s point of view, I suggest that, in a globalized world, a one language policy is not only problematic from the point of view of fair power relations and equal participation opportunities, but it also weakens science as a whole by excluding ideas, perspectives, and arguments from being voiced and heard.
How can projects produce evaluation and communication strategies in tandem? Why should they even try? A major benefit of helping projects produce evaluation and communication strategies at the same time is that it helps projects clarify their theories of change; it helps teams be specific and explicit about their actions. Before returning to the benefits, let us begin with how we mentor projects to use this approach.
Incommensurability is a recognized problem in interdisciplinary research. What is it? How can we understand it? And what can we do about it?
What is it?
Incommensurability is best illustrated by a real example. I once co-taught a class with a colleague from another discipline. Her discipline depends on empirical analysis of data sets, literally on counting things. I, on the other hand, am a philosopher. We don’t count. One day she said to our students, “If you don’t have an empirical element in what you’re doing, it’s not research.” I watched the students start nodding, paused for half a beat, and volunteered, “So, I’ve never done any research in my entire career.” “That’s right!” she replied, immediately, yet hesitating somewhere between a discovery and a joke.
How can we effectively engage in the practice and art of science communication to increase both public understanding and public impact of our science? Here I present five principles based on what I learned at the Science of Science Communication III Sackler Colloquium at the National Academy of Sciences in Washington, DC in November 2017.
1. Assemble a diverse and interdisciplinary team
Scientists should recognize that while they may be an expert on a particular facet of a complex problem, they may not be qualified to serve as an expert on all aspects of the problem. Therefore, scientists and communicators should collaborate to form interdisciplinary scientific teams to best address complex issues.
Science is like any other good or service—it must be strategically communicated if we want members of the public to accept, use, or support it in their daily lives. Thus, research scientists need to partner with content creators and practitioners in order to effectively share and “sell” scientific results.
Collaboration often improves decision making and problem solving processes. People have diverse cognitive models that affect the way each of us sees the world and how we understand or resolve problems. Adequate “thought world diversity” can help teams create and communicate science that is more creative, representative of a wider population, and more broadly applicable.
In part 1 of our blog posts on why use patterns, we argued for making unstated, tacit knowledge about integrated modelling practices explicit by identifying patterns, which link solutions to specific problems and their context. We emphasised the importance of differentiating the underlying concept of a pattern and a pattern artefact – the specific form in which the pattern is explicitly described.
How can modellers share the tacit knowledge that accumulates over years of practice?
In this blog post we introduce the concept of patterns and make the case for why patterns are a good candidate for transmitting the ‘know-how’ knowledge about modelling practices. We address the question of how to use patterns in a second blog post.
What does the word ‘pattern’ mean to you? And how do you use patterns in addressing complex problems?
Patterns are repetitions. These can be in space, such as patterns in textiles and wallpaper, which include houndstooth, herringbone, paisley, plaid, argyle, checkered, striped and polka-dotted.
The pattern concept can also be applied to repetitions in time, as occur in music. Those who know the temporal patterns can classify a piece of music as a blues, waltz or salsa. For each of these types of music, there are also classic dance steps, that usually go by the same name; these are patterns of movement in space and time.
These examples get to the idea that patterns can be viewed more generally as any type of repetitive structure or recurring theme that we can look for and potentially recognize or discover and then assign a memorable name to, such as “houndstooth” or “waltz”. Recognizing the pattern may then indicate a particular course of action, such as “perform dance moves that go with a waltz”.
The ability to recognize a pattern and then take appropriate action is something that we associate with intelligence.
As someone who works with scientists, journalists, advocates, regulators, and other types of communication practitioners, I see the need for translational scientists who can navigate productive, start-to-finish collaborations between such groups on a daily basis.
This translation involves the use of new, more integrated approaches toward scientific work to confront wicked environmental problems society faces.
In spite of this need, cross-boundary communication poses a major stumbling block for many researchers. Science communication requires engagement with potential beneficiaries, not just a one-way transfer of information.
I don’t see the world in pictures. I mean, I see the world in all its beautiful shapes and colors and shadings, but I don’t interpret the world that way. I interpret the world through the stories I create. My interpretations of these stories are my own mental models of how I view the world. What I can do then, to share this mental model, is create a more formalized model, whether it is a simple picture (in my case a very badly drawn one), or a system dynamics model, or an agent-based model. People think of models as images, as representations, as visualizations, as simulations. As tools to represent, to simplify, to teach, and to share. And they are all these things, and we need them to function as these things, but they are also stories, and can be interpreted and shared as such.