 with us in the IPCC adventure. She is an expert in graphic design. And she was mainly dedicated to the co-production of the SPM figures with us. So she was another one that was babysitting as IPCC author. In this co-production, we had a lot of interesting discussion and exchange. I think we learned a lot, maybe also learned a lot a bit from us. And today she will just show you one of these examples to make you understand in which way these figures are in general, in which is the best way to use figures to communicate and how many things you have to think before producing the figures. Angela, the floor is yours. And thanks for being here. Thank you so much, Erika. I am not really sure if I was babysitting, or we were babysitting, or it was the other way around. But hello, everybody. And thanks a lot for having me today. I was really hoping I would be able to travel to Italy and meet all of you, and especially seeing in person many people that I've had the honor to collaborate with. So I'm sorry I didn't work out. But I hope this virtual talk can still be useful. And if there are also thoughts that you would like to share that we do not manage to discuss in the Q&A, just feel free to reach out or to channel through Erika. So my name is Angela Morelli. And I have a background in engineering. And while I was studying engineering, I was often dealing with information presented in a very analytical way. Sometimes easy to read, sometimes less, sometimes beautiful, sometimes less beautiful, sometimes easy to understand, and sometimes really hard to decipher. And I can recall the sense of frustration for not understanding. Because not understanding makes you feel disempowered and unable to make good decisions for yourself, for the people you love, for the people you lead. So this is why, after my degree in engineering, I decided that I would study design. And I did an MA in industrial design. And I worked at the studio. The Japanese designer is also in Milan. And here I have learned the power of stories, the importance of beauty and sense of wonder. And after navigating in the world of engineering and industrial design for more than a decade, I entered the world of information design by doing a master in information design at Central Samaritans in London. And I realized that I was finally in my element. But my passion was not in information design or data visualization per se, but in the impact that we can have by communicating with clarity and beauty, the impact that we can have by co-designing with our audience and the scientists, the impact that we can have by considering the long-term effect of our design decisions. So when I am asked what I do, I answer that I am an information designer. And the company I run, Info Design Lab, is a socially conscious information design firm. So Info Design Lab is a small company. It is a lab. So we collaborate with partners and organizations who flow in and out depending on the projects we work on. We work with scientists and activists with the public and private sector, with universities and the media. We teach a lot and we run courses. And most of the time, the goal of our projects is to co-design visualizations that empower specialists and non-specialists to make informed decisions about the big challenges we face as a society from education to health, from social justice to climate change. And we have collaborated with the ITCC on three reports. And this collaboration has that really defining role for us professionally. So today, I would like to focus briefly on the way we work and on the tools we use, especially touching on the intent, the visual narrative, user engagement, and the slide book. So hoping that you can take away some useful things. So let's start with the way we work. For those of you who are not familiar with the discipline of information design, well, it has been defined in many ways, but this is a really good definition by information designer, Nathan Shedrov. Information design addresses the organization and presentation of data. It's transformation into valuable, meaningful information. So the goal is to produce products or experiences, visuals, narratives that can help readers navigate complexity in order to understand, in order to make decisions. As information designers, our goal is to design in order to create meaning for an audience. So if the goal is to create meaning, hopefully empowering our audience to see patterns, for us, learning how the audience will use and receive the information and understanding the context in which the information will be processed is key if we want to reach their mind. On the other side, we have the authors of the data and scientists listening to their needs and the ideas is equally important. As designers, we need to really dive into the content with the guidance of the content experts and we need to build a deep and shared understanding of the nature of the data and the science that underpins it. For data visualizations to be effective decision-making tools that help us learn and understand the design and production processes are not trivial and research insights from the fields of design, cognition, psychology and behavioral economics, as well as the increasing robustness of the human center than participatory design methodologies can guide us in designing data visualizations that enhance understanding, empower the final users and support informed decision-making at all levels. So the world of co-design really encapsulates a way of approaching data visualization that ensures this deep and shared understanding between those that create the visuals like information designers, content experts, cognitive scientists and the audience on the other side. So co-design is how we work. Now in the process of turning data into information presenting it in a way that becomes knowledge in the mind of the users and hopefully help make important decisions and ultimately inspire change, things can get complicated because of the collaborative and iterative nature of the design process that involves different users, scientists, designers, stakeholders and because it is really not a plug-and-play process, we have to adapt to different challenges and to the different projects we work on by really embracing both the scientists and the audiences. So the co-design process is a collaborative process based on iterations. So each iteration is usually based on three cycles, A, B and C and in each cycle there are design meetings and design work. And the process is not, as I said, a preconceived formula and depending on the number of people involved in the deadlines and depending on data and the challenges, we adjust and tailor things to meet the needs of different stakeholders. So what you see here is the design process for a project that required one iteration where we brought the brief with the client. There is an initial part of research and the red circles represent moments of user engagement. So it's about designing in a way that minimizes assumptions about our users allowing other stakeholders to be part of the process. When we worked on the data visualizations for the IPCC reports in the ER6, we had three iterations in the design process. The first one after the expert and government review of the second order draft, a second iteration after the final government review of the final order draft and the third iteration during the approval. And these iterations were carefully planned together with the IPCC in sync with the drafting timeline. And the red dots here is where we do the in-depth conversations to check the needs of the policy makers, follow-up conversations to quick reality checks during the design iterations, the user testing to measure if the visualizations fulfill the intent, review comments on the visualizations and content groups during approval. And if you're interested in the co-design process with the IPCC, we have written an article in Spring and Nature part of a collection called the Climate Change Communication and the IPCC. But what are some of the tools that we use to facilitate the co-design process? So the tools I'm going to share now might sound familiar to some of you. For us, these tools are the building blocks of the co-design process. And all the stakeholders involved in the design process will eventually master them. So there is an important training component in having these building blocks at hand. So we use these tools when we work on any project or when we teach in universities or in the research organizations and the toolkit keeps expanding and it helps us create learning journeys for our clients, namely collaborations where we guide the clients through all the stages of the design process while they co-design with us. So this toolkit becomes their toolkit as well. Now, we can say that a really important building block of this toolkit is the intent because with no clear intent, it will be hard to build a good visual narrative. So the data we work on can differ a lot depending on the projects. We can work on qualitative and quantitative data on small or very large data sets, on data that consists of numbers or words. And of course, the nature of the data set impacts the final results. But regardless of the nature of the data, it is always healthy in a way to start the process with an in-depth discussion and the shared understanding of the core pattern that we want our readers to see within the first minutes, they will look at the visualization. And we call this core pattern the intent. So the intent is the goal that the visualization aims to achieve. So we need to ask ourselves, why am I creating this chart? What is one thing that I want people to remember? What do I want people to do with this information? So you should always be able to write a clear intent that describes in one short sentence what you want people to remember and the pattern you want to convey. So if we do not work consciously on the intent of the visualization, we might end up constructing a very wobbly structure. So have a clear intent and use it to write a good deadline. This chart published in the New York Times shows how summer temperatures have shifted towards more extreme heat over the past several decades. Now, the title is really powerful. It's not your imagination, summers are getting hotter. And that's exactly what the charts will show. You have a bell curve and two visuals in this article and scientists compared actual summer temperatures for each decade since 1980s to a fixed baseline average. So across the Northern Hemisphere in the baseline period, most summers were in what they called a near average or normal range, the gray area. A third were considered cold, the blue ones and the third were out to the red ones. So since then summer temperatures have shifted drastically between 2005 and 2015. Two thirds of the values were in the hot category and nearly 15% were in this new category extremely hot. So we are keeping the promise. So it's not your imagination, summers are getting hotter. And this is the first visualization of four that we have designed with scientists that they sent it for climate change research in Norway for the exhaustion project, which is a four year project that involves several European partners that's still ongoing. So we breed climate change. It's like a metaphor that tries to hook the reader. And then the intent, namely the core message we want to convey is written in this annotation along the red circle. Climate change worsens the health impacts of air pollution. So we try to illustrate the intent by visualizing the flow of air pollution and greenhouse gases that cause climate change. And what happens is that climate change worsens the health impacts of air pollution through heat waves, pollutants, wildfire and we breed these effects that goes straight into our lungs and our heart. And same project with a new set of visualizations released last year where the intent is to show that more people will die of heart and lung diseases in our cities when high temperatures are combined with a high level of air pollution. So in order to show the intent, we illustrate the change in number of deaths and from heart and lung diseases by different levels of air pollution. Same project and a qualitative data set for this visualization on adaptation to compete that policies that increase the ability to adapt to climate change and decrease vulnerability can serve as a protective shield for citizens health. Similarly in the past IPCC summary for policymakers we worked on the physical science spaces. Each visualization has a very clear intent and the intent becomes the title of the visualization. The intent becomes an entrance to help readers take their bearings. So human influences warm the climate at a rate that is unprecedented in at least the last 2000 years. And this is the intense family tree for the 10 visualizations we designed for the last IPCC report with the summary for policymakers the physical science spaces. So basically it shows the history of the intense. The more we progressed with the visualizations the better we understood the key pattern the more we could refine the intent and so on. In most of our projects one of the tasks is to actually facilitate the discussion around intent together with the scientists. And defining the intent is part of a very messy process because defining a clear intent can require some time and intent can evolve based on reality checks with your audiences and based on user testing for instance. So the discussion around the intent usually starts when the data are not finally yet. Sometimes these discussions can even start when the data do not exist at all. It is all about trying to talk to the scientists and the audiences and see things from their point of view. So we usually start by asking the scientists what they think the intent of the visualization will be or could be. They have to complete this sentence. The intent of this visualization is to show that. And there is a reason for the syntax of this sentence because we do not want to have a description of the possible variables that we are showing in the visualization. But we want to understand what the key pattern is that the key pattern that needs to be communicated. So let's go back to the visual of this visual for a second. Here the intent is communicated through the title. This is the core takeaway. It's unprecedented and it's a cause by humans. So in order to illustrate the intent we need to visualize of course a number of variables in a certain way. So we use two panels and we try to convey this fast and unprecedented rate and the fact that is caused by humans. In order to visualize the intent unprecedented and caused by humans, we use the changes in global surface temperature. That's the variable we show. But that's not the pattern unprecedented and human cause that is described in the intent. So the intent is not a description of the things you show but a description of the key pattern that you want the users to see in the first minute they look at the visual. So that's the intent. And if the intent is the goal the visual narrative is what the visualization shows and how it looks in order to fulfill the intent. So the intent is what guides us in defining the atoms of the narrative. The visual journey that we build for the readers and what to include and what to leave out. If the intent is the steering wheel that's how I usually think about it. The visual narrative is the type of journey that we create for the reader. So we need to ask ourselves how do I keep the promise described in the title in the intent? What type of chart do I need? What data? What type of organization? How do I use color, typography, space, annotations to fulfill the intent? Having a clear intent is like having a benchmark. You basically can measure every sketch, every visual decisions towards the intent. If we go back to the visualization we breathe climate change for a second where the intent is that climate change worsens the health impacts of our pollution. The intent there helped us decide if our structure, layout, chart type, words were consistent across many different sketches until we go to the final one. And here you can see a static version of the visual a version for Twitter and an article that uses the visualization. So same intent is visualizing slightly different ways according to the context. Now defining intent and visual narrative in a co-design process a bit like riding a roller coaster it's fun and rewarding but it can also make you dizzy. So most of the time at the end of the ride you remember the joy but you need to swallow these 360 degree loops more of them, several of them. So the co-design process behind the figure SPM-9 for the physical science basis sometimes we call it the skyline has been a very interesting ride probably a little bit painful for many of you in the audience. And I kind of like hope that the memory of the pain faded away but there are some important lessons that we have learned. So on one side we receive the mandate to co-create figures for the summary for policymakers that were visualizing the synthesis of the assessment and one core message. But on the other side, we all realized that the nature of the data in this figure and the challenge of convening a multi-dimensional data set and the regional specificity required many more conversations with the scientists than we expected. So the figure you see in the summary for policymakers is in fact the panel B of a richer visualization that has been included in the technical summary. So I think that this figure just reflects the tension between two sides. On one side, the attempt to convene a multi-dimensional data set. And on the other side, the attempt to convene one single message. So on the right hand side, with the skyline the figure convenes the fact that multiple climatic impact drivers are projected to change in all regions of the world. And the purple-orange bar chart is showing the number of regions where each climatic impact driver is changing with higher or medium confidence. And that's happening in all regions. But on the left hand side, the regions of the world are grouped into five clusters, each one based on a combination of changes in climatic impact drivers. And on the left hand side, you see a visual that is more exploratory, I would say, where you need to dive into the different data points and navigate through different narratives. So on the left hand side, you have a more exploratory figure. On the right hand side, you have a more explanatory figure. And this is an important aspect when it comes to visualizing data. So data visualization can be seen as a journey that we build for our audience. And this journey depends on how well we know the audience, how clear your intent is, how you will use size, color, layout, typography, labels and text, all these to guide users through this journey where they process the information. And anytime we design a figure, we need to ask ourselves, what type of journey are we trying to build for our readers? Because ultimately, we want to create something meaningful for them. And in order to do that, it is important to actually understand the journey they need. When it comes to this journey, it might be useful to know that data visualizations can be placed along a continuum of author-driven and reader-driven approaches. A purely reader-driven approach has no prescribed ordering of images, no messaging and supports tasks such as data diagnostics, pattern discovery, hypothesis formation, you can think about Tableau, for example, you can think about dashboards. So the journey is an exploratory journey. A purely author-driven approach has a strict linear path through the visualization, relies on annotations and allows the readers to grasp the key pattern within the first few seconds they look at the figure. So the journey is an explanatory journey. But in fact, examples and most of examples, there is a research that shows and most of examples of narrative visualizations and data visualizations can be a combination of author and reader-driven approach. And it is so important to decide what type of experience we want to design. If we want to let the readers explore the visual arriving at one zone conclusion, or if we want to convey a really clear intent in a very author-driven way. So this table was the starting point of the co-design process for the ESPN-9. And this table is a very exploratory tool, but quite efficient. We spent a lot of time discussing the table and discussing all the different layers and trying to kind of like elevate key messages from it and then going back. So we have a collection, we have a slide book of 200 pages of sketches around this figure. But there's something on a table. So we tend to think that tables are boring, but in some cases, tables can be efficient to can be several dimensions. So when it comes to regional aspects, they might be useful visual tools. And there is also a lot of design literature on table design, actually. So it all depends on what type of journey we are building for our readers. So readers will try to build a narrative out of what we present. And this journey resembles the shape of a mountain on which we travel upwards and then downwards. So we should take care of the steps of this journey, of how we make sure readers get engaged and how we convince them to travel upwards, staying engaged until they see the peak of your story and the intent. And then we should take care of how we can walk them down the mountain without throwing them off the cliff, giving them a feeling of conclusion and giving takeaways. So to sum up a clear intent, namely the clear message that we want to get across helps us create a clear hierarchy of the information and helps us decide what we should include in the figure in order to fulfill the intent, namely the number of variables, the categories, the different dimensions. And now to organize them so that the intent can be visually clear. This allows us to arrive at a clear visual narrative for our readers by using design and cognitive principles, color and typography, layout and annotations. The little red dot in the mind of the readers is our purpose. We hope to turn information into understanding. And if our goal is to enter the mind of the audience, if the goal is to move our audience from understanding to action and hopefully change, if our goal is to create meaning, it is important to plan for user engagement. Because it is exceptionally hard to create valuable communication for our readers when we do not know anything about them. So whether we use in-depth conversations to understand their needs or surveys to test design solutions, our suggestions is to always find a way to dive into their world. And whether we work on small projects or large projects like the IPCC wants with many stakeholders, we always plan for user engagement. In projects like the IPCC, confidentiality applies to all the scientific data and to every information created or exchanged during the IPCC drafting process. So things such as user involvement is always a very tricky thing. But in the design team, we had a group of incredible cognitive scientists led by Jordan Harold from the School of Psychology and the Tindall Center for Climate Change Research. And we collaborated with them for the user testing sessions. Like this one performed with 148 participants testing out different formats for the visualizations. So last tool for today, to manage this complexity, we have to create tools to record the history of everything we work on to keep everybody in the loop, to ensure everybody is on top of the development from the beginning to the end. And in order to do that, we build what we call the slide books. So in our projects, we basically record the history of each visualization using these simple just Google slide presentations that are shared with authors and with everybody involved in the process. And these presentations are in continuous progress until the end. It is almost like working in track changes for the visualizations. So to conclude, if there is one thing that I would like to remember from this talk is that co-designing data visualizations with the readers and above all with the content experts can really help us create valuable communication tools because co-design can help us deconstruct complexity with the help of the scientists. And at the same time can help us meet our audience where they are because we involve them in the design process. So co-design is really centered on the involvement of everybody, all those who affect and are affected by the consequences of design. And that involvement can be really the foundation, I think for valuable communication. So thank you very much. That's me and I'm happy to answer questions. Thanks, Sandra. Any questions for Angela? So in the meantime.