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- All right, thank you. Well, I mostly I just wanted to start of by saying thank you, I appreciate so much the opportunity to be here, and appreciate the exploratorium being such a fantastic place for organizing these kinds of events. This is a really wonderful connection between some of the research community, and my field in informal science and museums, that I don't normally necessarily get to see some of this fantastic research that's happening out in the space. And I'm really looking forward to bringing this back to our museum and informing them about some of the things that we can do better in the future and our exhibits. If you haven't been to the Science Museum of Minnesota, we're a similar sized organization to the Exploratorium but a very different ethos. We also are a Natural History Museum as well as a Science Center. We collect objects. If you haven't been to a lot of Science Museums across the country, recognize how special and unique the Exploratorium is. And when we're talking about museum spaces, we're talking about this real variety these kinds of spaces. Like Jenn said, I'm the Director of Exhibit Media at the Science Museum. The project I'm gonna be talkin' about today is called xMacroscope, and I'll get to that a little bit more in the future. But, you can find a little bit of information about the project, it's just getting started, on https://www.xmacroscope.org. Everything that we're doing in this project, and everything that we tend to do at the museum is open source. So our code, our designs, our features that we're going to be talking about, our research, will be up there on that website as we go forward. The other thing that I wanna emphasize is that the project that I'm talking about is certainly a partnership. We do this work in partnership not only with museum professionals, but also researchers in the field. The xMacroscope project was inspired by some research into data visualization in a more formal setting at Indiana University, at CNS and Katy Borner leads that group, and you'll hear more about some of her work in a minute. Kylie Peppler at UC Urvine, and Joe Heimlich who's leading up some of our museum and research and learning at the Columbus Science Center, COSI. And my team at the Science Museum of Minnesota. So, why do we even care about data literacy in the museum setting? And Katy's got a great quote in one of her papers that I wanted to read that I think helps convey why this is an essential part of a informal science experience. "The invention of the printing press created a mandate "for universal textual literacy. "The need to manipulate many large numbers "created a need for mathematical literacy, "and the ubiquity and importance of photography, "film and digital drawing tools "posed a need for visual literacy. "Analogously, the increasing availability "of large datasets, and the importance "of understanding them, "pose a need for universal data visualization literacy." And I think when we're talking here, and we're all sitting here in this room, and we've been talking about data visualization for a few days, it's easy to sort of overlook the fact that we need to make a case that data visualization literacy, the importance of being able to understand and read these wonderful visualizations that we've been presenting, is actually literacy that we need to make a case is important. It isn't inherently in and of itself important, that these are tools for conveying a message, and we need to make an argument that they are useful tools in our space. I'm not necessarily a data visualization professional, I'm a Museum Exhibit Designer, and it is just simply one of the tools in our arsenal. And so I think it's important to recognize that this is an important tool, but we do need to make a case that it is an important tool in presenting messages about science. 'Cause at least from my bias perspective, that's what I'm really interested in, is getting people more engaged and excited about science. So, one of the things that we realize is that we know a lot about how people understand data visualization in the formal setting, in the classroom, and in research labs. And there's actually a ton of research, we've heard a lot about it this week. But, what do we know about the more informal setting? What do we know about how people read a thing like a giant map projected on the floor, or printed on the floor that represents human migration across the globe in an exhibit about race? It turns out that there's not a lot of research into the learning and education that is happening in the museum settings. There is some, and we've heard about some of it referenced today, and we're hopefully producing some right now. But, part of the reason I think NSF funded our project, is 'cause we could make a compelling case that this setting is a unique environment. And I think we heard about that the other day from Tamara talking about how sometimes certain research findings that you'll get in the lab, turn out very differently when you do them in a hallway with a lot of people walking by. You'll notice later in a picture, I've got one of our exhibits installed in a hallway with a bunch of people walking by. And so one of the things that I think we have learned from some research that Joe Heimlich has done, is that peoples familiarity, oh excuse I'll go back one. People familiarity with various data visualization concepts is very low. I hate to be a bummer, but like many of the visualization techniques and the grammars that we are using are unfamiliar. It doesn't mean that they're bad. But, they are new. So we should at least recognize that when the majority of the people that we walk past on our way to lunch see some of these techniques that we're presenting in this room, it might be the first time they've ever seen them. So that the first task that they need to do is understand how to read it at all, in the context of something. And to think about how that works. The other thing to keep in mind, and I really appreciate Joyce making this point, is that oftentimes that setting matters a lot in how they're perceiving this in the informal setting. That 45 seconds that visitors on average spend at an exhibit component is a barrier if something that is entirely new and that's the first time that they're seeing it. So we have to keep that in mind. I would like to make sure that we keep understanding that are some very compelling museum experiences that people spend 20 minutes at and fall in love with and will think about for the rest of their lives. But, we do have to keep in mind that there are many of those things that if we want visualization to be successful in the museum environment that average is an important driver. So, I wanted to think a little bit about some of the characteristics of a museum experience that we might need to consider when embedding a data visualization. And compare it a little bit to more of like a formal setting. One of the things that I think many of the visualizations that we've seen this week that we think are really compelling, we might at first seen in a very intimate environment. We're sitting at home, on our own personal device, and we're investigating it. In the museum, that's a social experience. Whether you came by yourself or with your family, you're doing that with other people around you, and everything that implies. Sometimes I like to think of museum experiences as very ephemeral, just like the web, maybe some of the challenges that the New York Times faces with a link is like we have these wonderful, amazing, distracting things happening going on next to you, and that could draw you away from an engaging data visualization experience. Oftentimes data visualizations in a sort of at home or a formal setting are didactic. They're really trying to tell you one key sort of message. Or as in many museum experiences, we're trying to go for an experiential sort of learning. Many of the other things that you've already done are experiential. Data visualizations in many of these mediums are primarily visual and in our environment, we're really going after experience. Certainly visual communication is a big part of what we do, but we're also like thinking about the fact that people are trying to have an experience for the day, and that changes how we might construct a visualization. The other one that I really wanted to get on is the private versus public. We are talking about personalization this week a lot, and thinking about how people might personalize a data visualization. So something like putting in your zip code to window down a map to a specific geographic area. That seems like a very easy thing to do, right? So, like nothing really big deal to put in your zip code in a public arena. But, we recently developed an exhibit about race that allowed you to take a quiz that represent your level of bias associated with different images. And, to build this exhibit experience we really had to keep in the fact that you're not doing this at home. You're doing this in a public space, you're imprinting your information about something that is likely visualizing something about yourself that is deeply meaningful but maybe not something that you want somebody seeing over your shoulder. So, we have to keep that in mind when we're adding personalization layers to these kinds of things. Let's skip past a couple of these. So, how are we addressing this in the museum setting? What we're doing is developing a open source platform, it's both a software and exhibit development platform called xMacroscope. xMacroscope is sort of the generation of this tool that Katy Borner's group developed called the Macroscope. It's this idea that you need a microscope to see sort of hidden small things in the world, but you need a macroscope to see these broad things that data visualizations can tell us. And xMacroscope is the next version of this that's focusing on the museum setting. The goal with this project is to build a platform that could be used for different museums, not just our place. Could actually be a platform hopefully someday as successful as Science on a Sphere, and but is not just sort of software, but is also an exhibit platform. It is both a technology development project, so we're actually writing software, databases, techniques for that will make it easy for museums that don't have technology teams to implement this on exhibit kiosks. It's an exhibit development platform, thinking about how do we build visualization between multiple different places in the museum and connect them together. And it's also a research project to understand how visitors are learning at these different settings with data visualization. So, we knew that the data visualization was not going to be the lead for the visitors, so we decided to build it around this project called Run. We have an existing exhibit experience called Sportsology, which allows you to sort of see the science associated with your body moving in sports, and we have a very popular exhibit that has nothing to do with data visualization, yet, called Run where you get to race in this case, T.C. Bear, the Twin Cities baseball mascot. You get to potentially race a T-Rex, or various other athletes. And it's incredibly popular, kids will just go through it again and again. They race their family members, and our goal here was to both from a funding perspective, to do some research around a built experience. So we didn't need to build something totally from scratch. But, also to hang a data visualization, a complex thing that we know that visitors are not necessarily inherently familiar with, around an experience that we know that they love already, and where they're generating data through their own body movements. So it's inherently personalizable, and where there's a great deal of hold time. So, in what we're proposing for the xMacroscope experience and what we're building right now is in the Run exhibit, is you'll see here that visitors line up, they enter in some information about themselves, where they're from, they're height, other different characteristics that they might want to visualize associated with their run, and then go through a race and then go to a make a vis station where they're actually going to start creating a visualization from the data that they generate. One of the biggest challenges associated with this is how do they find themselves in that data? And so this is where that exhibit development task comes in. We're sort of trying to figure out various different mechanisms to say if I start of in a start kiosk, and I run 60 feet down a track, and then I wonder over to another exhibit kiosk, how do I know that the data point that showed up there is me? I'm number 101, and I'm male and how do I find myself? So, we're experimenting with different ways, like barcodes on a wristband that you might actually scan at various different places. Unique identifiers or avatars that they might recognize. We're trying to make the process as smooth and simple, so that they can find themselves in the visualization without having to make that a frustrating exhibit experience. We don't want to make anything detracting from the fun. So, we've begun doing some initial visualization research and learning. So, we've built this make a vis station here, and a really simplified version of the Run exhibit at COSI in Columbus. And this is that picture of the exhibit in the hallway where people are trying to run past. It's sort of a temporary thing. This is some of the exhibits some of the challenges of doing this kind of research in a museum setting. We have to find things for temporary exhibits. You'll notice it's just sort of a fold up table, and testing things out. And we're starting to find some interesting results. It's designed based research, so we're seeing research results, improving on the data visualization interface on the make a vis screen, trying to figure out what works for visitors and what doesn't. And we'll be reporting all those research findings at the https://www.xMacroscope.org website. And we'll be presenting a few of the sort of the schematics and the technical aspects associated with it. There's nothing really to read here other than just recognize that building museum technology is complex. All right, well like I said, you can find all the information about the project on the website there. You can also find it on our Github page, and thank you.