TOOLS AND INSPIRATION FOR A
VISUAL LINGUA FRANCA
About Braintrust and this publication
Braintrust is an academic think tank whose primary purpose is to maximise the value of academic knowledge produced by students and graduates. We provide a digital solution for knowledge sharing through braintrustbase. com where students and graduates can share and make visible their knowledge production and academic skills towards one another as well as employers, researchers and anyone else who might be interested. We also offer live solutions for knowledge sharing, interdisciplinary co-operation and skills assesment through consultancy and workshops.
In this e-book we have put together reflections and insights about new tools of communicating academic knowledge across disciplines and towards a broader public. With this, we wish to inspire readers â€“ students, researchers, teachers and other communicators alike â€“ to use and further develop these tools in communication activities they might organize or take part in. Do you want to know more or host a workshop in collaboration with Braintrust? Go to braintrustbase.com or contact us at firstname.lastname@example.org
On Hain Mountain, Bohr had spoken of the challenges they faced. ‘These models,’ he had said, ‘have been deduced, or if you prefer guessed, from experiments, not from theoretical calculations. I hope that they describe the structure of atoms as well, but only as well, as is possible in the descriptive language of classical physics. We must be clear that, when it comes to atoms, language can only be used as poety. The poet, too, is not nearly so concerned with describing facts as with creating images and establishing mental connections.’ Heisenberg decided it was now time for a new language.
From “The Quantum Story: A History in 40 Moments” by Jim Baggott,
...the participants of many academic disciplines, particularly those which do not traditionally use visual models as part of their problem solving toolboxes, could benefit from methods combining analogising and visualisation at all stages of the academic process – from problem formulation to dissemination.
Semantic challenges in communicating academic knowledge As the importance of an interdisciplinary approach to problem solving is being recognised in both learning institutions and the workplace, these challenges of specialization and semantics can hinder progress in this field.
Interdisciplinary co-operation between academics As an academic discipline reaches a critical level of specialization, semantics and the precise definitions of terms can become ever more prevalent. While this is an obvious and necessary characteristic of clarity and understanding within that discipline, it can mean that interaction between other disciplines – even those which are relatively similar – becomes increasingly difficult; disciplines can end up becoming foreign islands of knowledge.
Knowledge dissemination to the world outside academia As well as challenges on an inter-disciplinary communication level, there is also a problem in communicating research and knowledge to a wider audience. This problem is often put down to the suggestion that the field of research is too complex and specialised for a lay audience to be able to comprehend. Simplifying it to a level which might be comprehensible would trivialise the research and render it almost meaningless or – worse still – inaccurate.
At the same time, as knowledge within a discipline becomes ever more complex, meaning only those with a similar education or research background will be able to fully comprehend it, the more general relevance and applicability of the sum of this knowledge can sometimes be lost.
Image Source: Braintrust
Posters for a Braintrust Interdisciplinary Knowledge Lab at University of Copenhagen. Participants weere encouraged to view complex problems from an interdisciplinary perspective.
Many academics have neither been trained in, nor seen the benefits of, finding methods for communicating their complex research in ways that could be understood by a general audience.
that effect change, without the need for reductionism, and sometimes even the opposite. Two of the examples come from engineers, one from a nurse/statistician, and one from a theoretical physicist; hardly typical of those from whom one would expect masterpieces of visual communication.
Braintrust aims to help academics and their tutors who wish to overcome these challenges, by providing new tools and ways of thinking and communicating academic knowledge. Specifically, it is our belief that the participants of many academic disciplines, particularly those which do not traditionally use visual models as part of their problem solving toolboxes, could benefit from methods combining analogising and visualisation at all stages of the academic process – from problem formulation to dissemination.
While each example has obvious merits in terms of dissemination techniques, they are also excellent templates and stepping stones for interdisciplinary communication, problem formulation and problem solving models. They form the bedrock of our aim: to help academics develop and build their own communication tools – what we call a “visual lingua franca”.
Included in this document are four landmark examples of how visual methods can improve understanding and communicate in ways
Lingua Franca/ Visual Lingua Franca Lingua franca – origins
academics. As disucussed in the previous pages, the negative impacts of spcialisation, complexity and semantic barriers suggest that new ways of communication both between academics and the wider audience can be beneficial. Our experience in helping academics create informal visual aids for cooperating across disciplines and disseminating their work to a more general audience can significantly reduce the impact of these boundaries.
With the explosion of trade on the Mediterranean from the 11th century and onwards, international merchants were faced the problem of communicating with each other. The acquisition of slaves from different regions compounded this problem. Over time, rather than learn multiple languages, a form of pidgin language gradually emerged. Essentially a simplified version of Italian and Romance languages, it also borrowed liberally from Turkish, French and Arabic. As trade moved westward, so did the influence of Portuguese and Spanish on the language. Traders, diplomats, pirates and slaves now had a method of communicating across boundaries. The Mediterranean Lingua France, or “Sabir”, (from the verb “to know”) became a vital bridging language for people unable to speak each others’ mother tongues.
Lingua franca: A language systematically used to make communication possible between people not sharing a mother tongue, in particular when it is a third language, distinct from both mother tongues. Visual Lingua Franca: A visual language systematically used to make communication possible between people not trained in the same discipline.
Visual Lingua Franca There are similarities to be found in the needs and challenges of these early traders and today’s
Image Source: Braintrust
Model building at the Braintrust Interdisciplinary Knowledge Lab at University of Copenhagen, April 2013. Creating analogies and freeing the Students from different disciplines from the language and semantics of their fields can enable better cross disciplinary co-operation, and allow them to view the problems and solutions in new ways.
Braintrustâ€™s tools for a Visual Lingua Franca Problem formulation
can reveal areas of the task which are not yet fully formulated.
The primary tool for problem formulation on which the Braintrust Knowledge Lab focuses is analogising. When an academic has identified their area of interest and the general direction of their field of study and research, there can be great benefits in finding analogous ways to describe it. The nature of these analogies can vary widely â€“ from the highly abstract to something more concretely similar from, say, another discipline. The ability to find an accurate analogy has three main benefits:
3 It can provide a template for later dissemination to a wider audience and serve as a vehicle for presenting the principal ideas.
1 It allows a specialised field to be discussed outside of that discipline. A robust analogy means that other interested parties should be able to grasp the most salient points and objectives. 2 It forces the academic to formulate the problem/ task in a concrete way, and as such ensuring they have a full understanding of the parameters and functions of their own project. The process of analogising can act as a form of elucidation and
a visual mnemonic aid for theorists so that they could more easily keep track of their calculations.
Once the correct analogy is in place, students will be encouraged – in co-operation with the visual consultants – to make a working model of the problem at hand. This model can be two or three dimensional depending on what is most appropriate.
3 The model can be used as a tool for explaining and describing their task visually, which can be even more effective than a verbal analogy. 4 It allows interdisciplinary co-operation in ways that are next to impossible using traditional methods of discourse. Once the analogy had been agreed upon and understood by all parties, they can then each “see” where in a project their roles, responsibilities, and opportunities lie. They can each add to the model independently, and enhance it. Crucially, then can ‘“bend” the original analogy when necessary, so that the model fits their specific task. 5 In a multi-disciplinary problem solving scenario, the model itself can act as the template for the solution.
The benefits of building a model are: 1 It allows the academic to have a working visual understanding of an abstract idea. This is particularly useful in disciplines where visualisation of problems is not traditional. In a three dimensional model, being able to literally ‘walk around’ the problem can develop and enhance an academic’s understanding of the task. 2 It can provide the academic with visual mnemonics (or memory aids) for the different aspects of the project. Feynman’s diagrams (see Example C), for instance, were primarily used as
Image sources: Diagram: Wikimedia Commons. Richard Feynman: hqdesktop.net/
An example of a Feynman Diagram (here showing the decay of a neutron into a proton), invented by Richard Feynman, below.
As a visual system it can serve as inspiration for how to develop some simple, basic rules and devices which can then be modified and added to, according to needs and diversity of disciplines.
Seeing the Unseeable There are many fields of research and knowledge where it might be supposed that visualisation is neither appropriate nor necessary. In case of theoretical physics, many of the pioneering physicists thought that, while it may be advantageous to convert complex mathematics to a visual model, it was simply not possible: the un-seeable was not capable of being visualised.
dimensional plane (representing time and space), the select audience was not immediately convinced. It took some years and the work of others before Feynman diagrams became the central tool for QED calculations. Because of the simplicity of the visual descriptions, the rules for using and interpreting them could evolve over time as the science evolved. Since then variations on these diagrams are used in a wide array of physics disciplines, far beyond what they were first devised for.
It was one of those pioneers, the remarkable Richard Feynman, who contested this point of view. Describing quantum electro-dynamical (QED) interactions involved sometimes pages of complex mathematical equations, with very few shortcuts. Calculations were bogged down by increasing complexity, and since calculations are the end product of a theorist’s work, progress was grindingly slow, and sometimes next to impossible.
Although himself a leading light in the field of theoretical physics, and a Nobel prize winner, it has been argued that his greatest contribution to science was this elegant representation of the infeasibly complex and the otherwise un-visualisable. Feynman Diagrams can serve as inspiration for visualisation of methods or conclusions in academic fields such as the humanities, that do not generally deal with the physically tangible as their field of study.
When Feynman unveiled his diagrams in 1948, which at once described complex mathematical formulae and visualised subatomic events on a two
Source: American Scientist, Volume 93
This diagram shows the importance of choosing the right methods to communicate the most salient aspects of one’s research and findings. In no way did it diminish or trivialise the mass of data and research which underpinned the graphs. It merely displayed it in such a way as to focus the viewer in terms of how that data should be interpreted. At the same time it managed to make the same data relevant to a wide audience of different disciplines, interests and backgrounds.
The mother of Statistical presentation Most of us upon hearing the name Florence Nightingale will conjure up the image of the “Lady with the Lamp”, a legendary nurse during the Crimean War more than 150 years ago. But Nightingale’s legacy goes far beyond the humane treatment of wounded and sick soldiers, and into the world of statistics, disease prevention, and crucially, how to communicate complexity in a way that will be both understood and acted upon appropriately. A copious and rigorous statistician, Nightingale recorded vast data regarding her patients from when she first arrived in Turkey in 1854, to her return to Britain in 1856. This data revealed something quite shocking. While the soldiers at war were – in theory at least – young, fit, well fed and healthy, at an age and condition where non-battle related deaths are at their minimum, they were dying from preventable causes at a much higher rate than the total population of Britain as a whole. Given the widespread poverty, the disease-ridden slums of the industrial north, high Source: The Visual Display of Quantitative Information, Tufte, Edward R.
child mortality rates, and low age of life expectancy, this ran counter to all intuition. Not only that, but the soldiers were dying of preventable causes at a ratio of 10:1 compared with those dying from war wounds and other casualties of war. Armed with these shattering statistics, Nightingale realised she had to convince the right people to act upon them. A crucial person to persuade was Queen Victoria, along with various members of parliament. But she knew well that countless pages of raw statistics simply would not be read or understood by such people. So she endeavoured to present the same statistics in a way where the underlying complexity of the statistics remained, but they could be digested readily and almost instantaneously. Nightingale’s development of “coxcomb” diagrams are a landmark in statistical representation. Even today, when we are bombarded with charts, diagrams and graphs, these look extraordinarily modern.
Image source: The Visual Display of Quantitative Information, Tufte, Edward R.
Florence Nightingale’s coxcomb diagram showing the “Causes of Mortality in the Army in the East”
did it diminish or trivialise the mass of data and research which underpinned the graphs. It merely displayed it in such a way as to focus the viewer in terms of how that data should be interpreted. At the same time it managed to make the same data relevant to a wide audience of different disciplines, interests and backgrounds.
Essentially a pie chart, where the total area in a given slice [a month] corresponds to the total number of dead soldiers, and the subsections within the slice divide the total into corresponding statistics [blue, preventable deaths, pink, deaths from wounds etc. and black, other deaths]. One can see almost instantaneously how preventable deaths are overwhelmingly the primary cause of mortality.
Today, such presentation methods are the norm, with graphs and pie charts adorning many papers and presentations. But it is an inspiring example of how the right methods can increase understanding and promote action. For more contemporary examples, see the methods of organisations such as Gapminder.org who are developing new ways of interpreting and presenting data.
This diagram has huge consequences whether one is a war strategist, a politician, a medical scientist, a humanitarian, or a member of the voting public. Its impact was massive. Queen Victoria and politicians alike took action, and led to a complete overhaul of how soldiers were treated, both from a practical level and a more philosophical level. The results of the actions taken were profound, while Nightingale herself became the first female Fellow of the Royal Statistical Society.
Source: The Visual Display of Quantitative Information, Tufte, Edward R.
This diagram shows the importance of choosing the right methods to communicate the most salient aspects of ones research and findings. In no way
This type of diagram can become an evolving template for both problem formulation, problem solving and dissemination of results.
Narrative and Interdiscipline A decade after Florence Nightingale’s landmark statistical work, a French civil engineer called Charles Joseph Minard created what is often regarded as one of the greatest statistical graphics ever produced. Charting Napoleon’s infamous campaign in Russia in 1812, Minard developed a whole new system for disseminating a temporal narrative which incorporated statistical data from diverse disciplines. The result is a masterpiece which manages to deliver complex data in an easily understandable way, while at the same time conveying the human tragedy which unfolded over the months of the campaign. Referred to as a “Sankey diagram”, this method for articulating movement or flow over time has been used countless times since, in diverse fields of knowledge, including engineering [indeed, it was subsequently named after Matthew Sankey, who in 1898 produced a flow diagram showing the thermal efficiency of a steam engine]. Minard’s diagram shows, from left to right, the Source: The Visual Display of Quantitative Information, Tufte, Edward R.
geographical movement of Napoleon’s army (at this point one large group) in beige. The thickness of the line represents the number of soldiers in the army, which at the left point constituted 422,000, as they converged by the river Niemen (which is also represented). As they moved eastward, we can see 22,000 soldiers leaving the main group and head northwards. As the beige line continues to become thinner, with important dates and geographical locations shown, it comes to an abrupt halt at Moscow, where the 100.000 remaining soldiers find the city deserted and devoid of food an supplies. From here the temporal narrative changes to right to left, and eastward to westward, and the optimistic beige colour is replaced with solemn black. At this point we can also chart the temperature over time, and the disastrous impact of the freezing conditions to the ill equipped army. We then see a group of some 20.000 soldiers who had earlier stopped at Polotsk to rejoin the main group, only for half of them to be slaughtered by the river Berezina.
Image source: The Visual Display of Quantitative Information, Tufte, Edward R.
Joseph Minardâ€™s Diagram charting Napoleonâ€™s 1812 Russian Campaign
Image source: wikimedia commons
The Thermal Efficiency of Steam Engines Sankey diagram, by Mathew Sankey 1898.
structure for flow, movement or process, different types of data, research and statistics can be added to it in an appropriate and dynamic way, meaning that while the overall information might become more complex, the diagram does not become less understandable. On the contrary, it can mean that a complex, multi-disciplinary narrative or picture can be created which would otherwise be very difficult to communicate in a coherent fashion.
We finally arrive where we began in the digram, the original 422,000 soldiers reduced to a mere 10,000. The genius of this diagram is the fact that we are being provided with so many different types of information and dimensions at the same time, and yet it is remarkably simple to follow. Indeed, what is perhaps most remarkable that it still allows us to be moved by the epic tragedy that this statistical chart describes. One can quite readily see that this approach to visualisation can be effective for both formal models (such as Sankeyâ€™s diagram, above), or as a more informal model for problem solving and discussion amongst people of different fields of knowledge and discipline. With an overall
This type of diagram can become an evolving template for both problem formulation, problem solving and dissemination of results.
F. H. Stingemore’s Underground Map Of London, 1933, printed by Waterlow & Sons Limited. Source: The London Transport Museum.
London Underground map, © The London Underground 2011. Harry Beck photograph: London Transport Museum/TFL
Left: early incarnation of the underground map., Right Harry Beck’s (pictured) simplified geomtetric version.
Simplifying to magnify By the beginning of the 20th century, the London Underground train system was already a vast and complex system with nine distinct lines which weaved their way to the outlying suburbs of the large city. Network maps, though constantly revised, always followed the tradition method of geographical representation, with the train routes superimposed onto a proportionally accurate plan representation of London and its surroundings. The inherent problem in this method was that, as the new routes extended further from the centre, covering more and more distance between stations, the maps grew larger, while the most complex and crucial portion – the city’s centre – became proportionally smaller. This, combined with the fact that train routes often followed the meandering roads of a medieval city, following a route on the map and deciding on where to interchange from one line to another became an increasingly difficult task. Harry Beck, a trained engineer working as a draughtsman at the London Underground, proposed
Source: Mark Ovenden, London Underground By Design
a new solution. Using the analogy of an electrical circuit diagram, he suggested that rather than try to emulate the physical proportions and geographical accuracy of the city and the network on a map, the journey should instead be seen only in terms of the number of stops and points of interchange. Assuming one knows the starting point and destination, once a person is inside the system, one should only need to know about the options and information within that system. All other information simply clouds and confuses. So geographical distances were eliminated, and straight lines which only travelled at zero, 45 and 90 degrees were employed. The map, perfectly scalable, was an instant public success, and is now a design classic. This method for showing complex networks is now ubiquitous the world over. The same principles can be used for mapping out and structuring data in such a way where the most crucial aspects of research become the most visible, and links between different types of information can be easily navigated.
Acknowledgements Authoring & layout: David B Earle
Editing : Sigrid Bjerre Andersen
The Visual Display of Quantitative Information Tufte, Edward R. London Underground By Design Mark Ovenden The Quantum Story: A History in 40 Moments Jim Baggott
Braintrust 2014. The e-book is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 3.0 Unported License.
Published on Jan 18, 2014
An introduction to Braintrust's visual methods for improving interdisciplinary co-operation and communication.