DesignScience Workshop presentation

Design workshop for researchers Cheltenham Science Festival: 21 Mar 2014 London School of Hygiene & Tropical Medicine: 27 Mar 2014 Imperial College London: 9 April 2014

Organised and presented by DesignScience Workshop Trainers Anne Odling-Smee Patrick Roberts Stefanie Schwarz

What is design?

DesignScience

WHAT CAN DESIGN DO FOR SCIENCE, AND SCIENCE DO FOR DESIGN? Anne Odling-Smee and Phillip Kent explain how the DesignScience group is working with scientists and engineers to improve understanding of design, and working with designers to improve understanding of science.

The goal is better science communication for the benefit of all.

WHY DESIGN? When you think of ‘design’, perhaps you think of cool chairs, designer fashion, or being artistic without practical function?

Perhaps you associate design with the worst excesses of branding and advertising, Mad Men, and corporate capitalism? In our work with DesignScience we have different ideas, but we recognise that overcoming stereotypical perceptions is vital and not without its challenges. Those working in the design field also exhibit misperceptions of science that need to be addressed. In our definition, design is concerned with ideas and problem solving on technical, functional, aesthetic, economic and socio-political levels. There is a classic definition (attributed to Neville Shute) of the engineer as a person ‘who can do for ten shillings what any fool can for a pound’. Through intelligent use of tools and resources, a better outcome can be achieved, and for less money.

What engineers achieve in the technical realm, so DesignScience aims to achieve in communication and public engagement for science. Design is perhaps best understood as being like a glue between someone else’s content and an intended recipient. We recognise that scientists do communicate with a variety of audiences all of the time.

Those of us who are professionally known as ‘designers’ differ in the degree of expertise that enables us to do this specific job more effectively across the complex variety of communication media now available.

COMMUNICATING SCIENCE There are many reasons to celebrate the progress that has been achieved in science communication and public engagement with science in recent years – especially here at the Cheltenham Festival. But news stories, such as the entirely predictable and harmful repercussions of the 1990s MMR/autism scandal that have lead to the recent measles epidemic in South Wales, indicate that we have a long way to go.

We see engagement as having two elements: ownership and participation.

British society today is at heart the product of science and technology developments going back hundreds of years. It is essential that the majority have a sense of ownership of this heritage as well as for a shared future.

Arguably the popular sense of ownership has become stronger in recent years – for example, we see clear public expression of identification with the science celebrities of television and radio. Engagement by participation is a far greater challenge. Both scientists and the public have reasons to be wary of it. We have seen a certain amount of ‘citizen science’, including in the mainstream media, however the participation is typically through observation and data collection

(many eyes, hands or feet) and not in data analysis, interpretation or theory-building.

You may wonder how a greater degree of participation could be possible given the asymmetry of knowledge and expertise. To be sure, we are talking about shifting the asymmetry to significant degrees, not removing it. Exploring this challenge is a major element of the work that the DesignScience group will develop over the next few years. Computing and computational thinking are important because one of the greatest barriers to popular participation in science is lack of mathematical knowledge. Without mathematical understanding, the theories expressed in mathematical form or the workings of data analysis are inaccessible.

Computers extend and restructure the ways in which it is possible to engage with the mathematical expressions involved in scientific ideas. Indeed, science and mathematics educational researchers have been exploring this for many decades, but the results are neither well known nor widely accessible. We currently have an unprecedented technological infrastructure of widespread personal access to computers, and electronic networks for exchange of information and social interaction. We need to build on that by devising educational resources and practices to change public participation with science and mathematics.

RISK: COMMUNICATING UNCERTAINTY Participation is crucial as a means of communicating uncertainty, and this is one of the key challenges for science communication today. Extreme weather events are on the increase and virulent animal flu viruses threaten the human population worldwide. The threat of earthquakes has been with us for millennia, and the scientific expertise now available is substantial. Yet communication of vital information about risk factors breaks down again and again; witness the recent prosecution of six scientists in Italy as a result of the 2009 l’Aquila earthquake.

How can we address the asymmetries of knowledge and expertise between scientists and the public? A powerful idea that we are working with is the potential of computers to simulate reality, in part using the mathematical models that are integral to the scientific understanding of phenomena.

It is all too easy to rationalise unlikely future events out of existence because we cannot live through them directly. In a virtual reality, everyone may participate and achieve new kinds of dialogue through the shared experience.

Scientists cannot be held responsible for all of the problems in science communication.

Communication is a complex, two-way

process. People may hear and understand a message yet not be able to act on it.

Scientists get fed up when they do their research, then are told they’ve got to communicate it. This is understandable when they lack sufficient expertise or support. DesignScience is trying to build meaningful relationships with scientists, technologists and engineers to make design and communication an integral part of the process of doing research – not just a part you tack on at the end as ‘impact’. To achieve this we have to first overcome our own challenge – that of communicating to scientists what design is and what it can do for science. We hope this feature goes some way towards achieving this, and to dispelling some of the unhelpful myths surrounding our subject.

We are also acutely aware that designers, journalists and public relations teams are not always sensitive or understanding of what science is, or of the needs and interests of scientists; so we are campaigning for a change of attitudes and the development of new learning opportunities and educational resources in this area. Indeed, we are convinced that the practice of design in general would be improved by incorporating more scientific approaches.

We see the totality of what we are doing as establishing a feedback loop between design and science that will build up as a significant force for change in science communication.

To find out more visit www.design-science.org.uk

Design process

Research question

Review

a) Theoretical b) Practical

Theory

Dissemination

what? why? who? how?

1. Text and typography 2. Colour 3. Imagery 4. Layout 5. Context 6. Production

1. Text and typography 2. Colour 3. Imagery 4. Layout 5. Context 6. Production

Choose a legible and appropriate typeface Typefaces with too much character can be illegible, distracting and communicate unhelpful, subliminal messages

Sans serif Serif

Capitalisation CAPITALISATION

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range left

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unjustified

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range right

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centred

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mixed

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justified

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Traditional science journal design

Modern newspaper design

10–12 words per line max

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Ut intium faceperchil id et maxim nos ad que que moluptat molorias alignim volo des ea volupic ipiendit officiis nienis dolenimil maio. Disit acesequi as sequi num raerspedis ni Ere, erumquibus doluptas eratur rerferio.

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Leading Too tight Otaes aut harciis as peria dolorernatem nimendi stiorep erenime imus asitio. Nam, optati volorem porror atusaepudam a apit omnisim rest, quiae. Ut intium faceperchil id et maxim nos ad que que moluptat molorias alignim volo des ea volupic ipiendit officiis Too wide Otaes aut harciis as peria dolorernatem nimendi stiorep erenime imus asitio. Nam, optati volorem porror atusaepudam a apit omnisim rest, quiae. Ut intium faceperchil id et maxim nos ad que Correct Otaes aut harciis as peria dolorernatem nimendi stiorep erenime imus asitio. Nam, optati volorem porror atusaepudam a apit omnisim rest, quiae. Ut intium faceperchil id et maxim nos ad que

Avoid

mixing typesizes

and typestyles

Type size 2 for secondary reading level

Type size 1 Type size 3 for info details

Avoid

over kerning and

underkerning

1. Text and typography 2. Colour 3. Imagery 4. Layout 5. Context 6. Production

Legibility

POOR

POOR

POOR

GOOD GOOD GOOD

Associations

Fire

Fire

Fire

Perception

Lottolab

RGB vs CMYK

1. Text and typography 2. Colour 3. Imagery 4. Layout 5. Context 6. Production

Image quality, ‘300dpi’

Visual metaphors

Visual metaphors

‘The price of metaphor is eternal vigilence’ – Lewontin, R. C. (2001)

Expose the process

such calculations often involve elements of chance/uncertainty and interactivity

Data visualisation programming can visualise such data, make it accessible and understandable, might enable to reveal patterns

visuals are easier to read than lists of numbers letter

frequency

A B C D E F G H I J K L M

.08167 .01492 .02782 .04253 .12702 .02288 .02015 .06094 .06966 .00153 .00772 .04025 .02406

N O P Q R S T U V W X Y Z

.06749 .07507 .01929 .00095 .05987 .06327 .09056 .02758 .00978 .02360 .00150 .01974 .00074

Data visualisation

Beautiful Science: Picturing Data, Inspiring Insight 20 Feb – 26 May 2014

Communicating uncertainty

Shared language

Why programming could be a shared language between scientist and designer

programming

scientist

designer

DesignScience

• Intentionally conflicting information: different opinions of doctors and evidence from personal research.

Probability simulator

Life-style modeller ('what if I don't have the operation?')

The user hasmodeller chosen three aspects Deborah's as inferred from Life-style (‘what if I of don’t havelife, thewhich, operation?’) the scenario information, might affect her lifestyle, were she not to have the © PhillipThe Kent, Dave Pratt and Ralph Levinson, Institute Education operation. clock depicts time passing whilst the red bar in the topof right corner oscillates up and down as her pain varies as a result of these

[Example 3]

Probability simulator

Probability simulator ('what if I have the operation?')

Š Phillip Kent, Dave Pratt and Ralph Levinson, Institute of Education The user has decided on an overall success rate for the operation of 70%; they have also chosen three possible consequential hazards and assigned

1. Text and typography 2. Colour 3. Imagery 4. Layout 5. Context 6. Production

Grids and space

Text image relationship

Type over image

Type over image

or Apple

Apple

1. Text and typography 2. Colour 3. Imagery 4. Layout 5. Context 6. Production

Mixed media

Evaluation

1. Legibility 2. Layout 3. Image choice 4. Context 5. Programs

Programs Professional design programs Adobe InDesign Adobe Illustrator Adobe Photoshop QuarkXpress

Free / affordable design programs Keynote (for Macs) PowerPoint Microsoft Publisher Microsoft Word Paintshop pro Corel Draw LaTex

References Asymmetric typography, Jan Tschichold, 1967 Design science, life and work of Will Burtin, Remington & Fripp, 2007 Grid systems in graphic design, Josef Muller-Brockmann, 1961 Helvetica, documentary film directed by Gary Hustwitt, 2007 Sign design, Mitzi Sims, 1991 Swiss graphic design, Richard Hollis, 2006 The new handmade graphics, Anne Odling-Smee, 2004 The visible word (legibility studies at RCA), Herbert Spencer 1968, 1969