WEBVTT NOTE Podcast: Data Stories Episode: Highlights from IEEE VIS'22 with Tamara Munzner Publishing Date: 2022-11-21T19:07:15+01:00 Podcast URL: https://datastori.es Episode URL: https://datastori.es/168-highlights-from-ieee-vis22-with-tamara-munzner/ 00:00:00.017 --> 00:00:04.888 Oklahoma University has this astounding history of science program and these archives. 00:00:05.077 --> 00:00:08.717 There was this part where he went into the climate controlled archives and brought out 00:00:08.717 --> 00:00:12.117 the books and there was this room full of books where we had to like, of course not 00:00:12.117 --> 00:00:15.789 touch, but look at these amazing source materials. 00:00:15.979 --> 00:00:36.357 Hi, everyone. 00:00:16.560 --> 00:00:35.760 Music. 00:00:36.747 --> 00:00:43.717 Welcome to a new episode of Data Stories. My name is Enrico Bertini and I am a professor at Northeastern University in Boston where 00:00:43.717 --> 00:00:45.997 I do research and teach data visualization. 00:00:46.802 --> 00:00:50.717 That's right. And I'm Mo Steffaner. I'm an independent designer of data visualization. 00:00:51.258 --> 00:00:56.317 In fact, I work as a self-employed truth and beauty operator out of my office here in the 00:00:56.317 --> 00:00:59.045 countryside in the north of Germany. 00:00:59.237 --> 00:01:05.734 And on this podcast, we talk about data visualization, analysis, and generally the role data plays in our lives. 00:01:06.317 --> 00:01:08.966 And usually we do that together with a guest we invite on the show. 00:01:09.637 --> 00:01:15.677 And that's the same today. But before we start, just a quick note, our podcast is listener supported, so there are 00:01:15.677 --> 00:01:17.517 no ads. 00:01:17.517 --> 00:01:21.677 But that also means if you do enjoy the show, you might consider supporting us. 00:01:21.677 --> 00:01:26.517 You can do that with the recurring payments on patreon.com slash data stories, or you 00:01:26.517 --> 00:01:31.129 can also send us one time donations on paypal.me slash data stories. 00:01:31.562 --> 00:01:38.466 Okay, that's right. So we can get started. The topic for today is a classic of data stories. 00:01:38.817 --> 00:01:46.271 I think we missed only once or twice. We're going to do highlights from the iTripoli Viz conference. 00:01:46.406 --> 00:01:52.557 And we have a friend of the show helping us to do that. We have Tamara Manziner with us. Itamara. 00:01:53.230 --> 00:02:02.655 Hi, thanks so much for having me. Great to see you again. Tamara, can you briefly introduce yourself and then we can dive right into the highlights from Viz? 00:02:02.988 --> 00:02:07.960 Sure. I'm Tamara Munzner and I'm a professor at the University of British Columbia Computer 00:02:07.960 --> 00:02:16.920 Science Department and I do visualization of many flavors. I am a long-term fan of IEEE Viz, 00:02:17.068 --> 00:02:23.480 first started going in 1991, so it's been a few years. And I'm a fan of data stories as well. 00:02:24.567 --> 00:02:28.280 Yes, we have a lot of things in common. Perfect match for the episode. 00:02:30.724 --> 00:02:35.640 So I thought maybe I should spend a couple of words explaining what iTripoli's. 00:02:36.549 --> 00:02:40.680 Is for any of our listeners who are not particularly familiar with it. 00:02:40.861 --> 00:02:46.680 So I try to believe this is a conference, international conference that happens every year. 00:02:47.082 --> 00:02:54.680 Since, as you said, Tamara, from the mid-90s or so. And no, early 90s, I guess. 00:02:54.680 --> 00:02:57.344 The first one is 1990. It depends how you kind. Yeah, yes. 00:02:57.680 --> 00:03:02.680 And it tends to be pretty academic. It's the main academic conference. 00:03:02.854 --> 00:03:08.680 But I like to say that it's not only academic. There are a lot of events and things that happen in there, 00:03:08.680 --> 00:03:17.680 in there, including industry tracks, and there's an art section. 00:03:18.000 --> 00:03:22.560 So over the years, it's been expanding in a way that is definitely, it has an academic 00:03:22.560 --> 00:03:26.277 flavor but it's not only academic. 00:03:27.080 --> 00:03:32.240 We tend to talk a lot about the papers that get published and presented there, but there 00:03:32.240 --> 00:03:34.235 are many other events going on. 00:03:34.400 --> 00:03:37.800 And I think today Tamara is going to talk about some of these events. 00:03:37.800 --> 00:03:45.680 There are panels, there are workshops, there are keynotes, there are awards, and many, many other things. 00:03:45.680 --> 00:03:50.007 As I said, there is an art track, an industry track. 00:03:50.106 --> 00:03:55.400 I also just want to say that summarizing what happened at Viz is basically impossible. 00:03:55.400 --> 00:04:04.680 We were going to talk about a tiny, tiny fraction of what happened there this year, but yeah, 00:04:04.680 --> 00:04:10.920 I think it's better than nothing and we will add links to videos and papers and documents. 00:04:11.181 --> 00:04:17.520 So definitely go to our blog post and check it out for if you want to dive right into 00:04:17.520 --> 00:04:20.039 if you want to see the details of what we're going to talk about. 00:04:20.975 --> 00:04:29.825 So Tamara, thanks so much for helping us with this. And I think we want to go through a number of different sections. 00:04:29.825 --> 00:04:33.686 And the first one is about keynotes and capstones. 00:04:34.245 --> 00:04:40.825 Yeah. So, you know, there's the obvious ones, which were the main viz keynote and capstone. 00:04:40.825 --> 00:04:45.533 And there were these sort of interesting parallels. So the keynote was Marty Hurst of Berkeley, 00:04:45.857 --> 00:04:48.297 and her talk was called Show It or Tell It. 00:04:48.828 --> 00:04:56.255 And it had this great framing context where, you know, using these large language models is now a thing. 00:04:56.465 --> 00:04:59.985 And so her framing context was that there were these dolly images as a bunch of the 00:04:59.985 --> 00:05:03.865 slide backgrounds, and she included the prompts that made them. 00:05:03.865 --> 00:05:10.745 And her whole point was that language should be considered co-equal with the visualization parts. 00:05:10.956 --> 00:05:17.025 It was all about this sort of text and images working together towards a goal. 00:05:17.025 --> 00:05:22.425 And you know, I very much liked it. It's exactly my jam. It's, you know, starting out with 00:05:22.425 --> 00:05:28.600 references to that Scott McCloud understanding comics, which still holds up to the test of, 00:05:28.745 --> 00:05:33.047 time, you know, all the way up to some book that I clearly have to read because it's called, 00:05:33.110 --> 00:05:38.065 Proust and the Squid with the tagline of we were never born to read. So I'm like, oh my, 00:05:38.116 --> 00:05:44.585 God, I have to read that. And I thought it was very nicely done. She really talked about 00:05:44.585 --> 00:05:48.999 language as a user interface for visualization and that there may be ways of using it that, 00:05:49.145 --> 00:05:53.780 instead of coding or a GUI, what if you can just speak. 00:05:53.825 --> 00:05:59.625 A theme that others also talked about is the speed of improvement of natural language methods 00:05:59.625 --> 00:06:05.765 with these huge models these days with GPT and Dolly. 00:06:05.765 --> 00:06:10.803 She really wanted to draw the attention of the Viz community to the way in which text, 00:06:10.875 --> 00:06:12.639 and visualization can work together. 00:06:13.105 --> 00:06:19.436 I thought it was very well done and very engaging. Yeah, I think it's a huge, huge topic right now, 00:06:19.508 --> 00:06:24.855 both from, as you say, text understanding by machines, but also text generation. 00:06:24.963 --> 00:06:29.865 And then we have the whole topic of annotation and accessibility where text plays another huge role. 00:06:29.865 --> 00:06:32.265 So I think it's probably one of the big... 00:06:33.047 --> 00:06:38.497 Big topics right now. And I guess she was a great match. She's a legend in information retrieval 00:06:38.497 --> 00:06:45.377 and text natural language processing, right? So yeah. And she's been involved in viz for eons 00:06:45.377 --> 00:06:51.268 and had one of the very early courses in InfoViz. Yeah, she's always been a little bit across. 00:06:51.448 --> 00:06:58.470 She also, she's also done some really fundamental work in viz. So it's really interesting. But yeah, 00:06:58.817 --> 00:07:06.177 I agree there's so much going on in NLP that is almost like having an effect everywhere, 00:07:06.177 --> 00:07:10.737 including in visualization. So I'm curious to see what is going to happen next. 00:07:11.857 --> 00:07:17.537 She was citing a few scary statistics that like a fifth of people in some studies wanted to hide 00:07:17.537 --> 00:07:24.177 the graphics so they can read the text better. I think these are inconvenient truths for the database 00:07:24.177 --> 00:07:27.493 community, but we have to acknowledge that, I guess. 00:07:28.366 --> 00:07:33.217 Yeah, there's all kinds of great stuff about what happens when people do see both, and 00:07:33.217 --> 00:07:38.458 how much text you want, and thinking about a dial of a little bit of text, a lot of text. 00:07:39.217 --> 00:07:42.537 Much to learn. So definitely a recommendation to watch this keynote. 00:07:42.537 --> 00:07:47.181 It's 45 minutes or whatever it is well spent. It's up on YouTube. 00:07:47.917 --> 00:07:51.177 What if we leap all the way from the keynote to the capstone? 00:07:51.177 --> 00:07:58.377 Those are the two bookends of the conference. Oh, it was so cool. So this guy, Cary McGruder. 00:07:59.257 --> 00:08:06.217 Who is this historian of science. So it turns out that although I did not know it before I got 00:08:06.217 --> 00:08:12.297 there, that Oklahoma University has this astounding history of science program and these archives. 00:08:12.802 --> 00:08:17.817 And he dug into the archives to come up with this talk on Galileo's telescope discoveries, 00:08:17.817 --> 00:08:20.697 thinking visually in the history of science. 00:08:21.462 --> 00:08:28.392 The capstone didn't just start with the capstone. It started the night before where they had an open house at OU and I was like, okay, yeah, maybe I'll see some demos. 00:08:29.006 --> 00:08:40.472 But there was this part where he went into the climate controlled archives and brought out the books and there was this room full of books where we had to like, of course, not touch, but look at these amazing source materials. 00:08:40.997 --> 00:08:45.772 And so seeing these physical books that you hear about them, 00:08:46.155 --> 00:08:49.272 but to actually see them with your own eyes and see the scale of some of them, 00:08:49.567 --> 00:08:54.950 some were huge in terms of fold-out things, that was pretty magical. 00:08:55.571 --> 00:09:02.472 And the talk itself, I really did love. I thought it was one of the top three capstones I've seen at Biz in 30 years. 00:09:02.719 --> 00:09:09.903 I feel like sometimes people just sort of, you know, they give their same old same old talk. 00:09:10.317 --> 00:09:14.172 And sometimes people actually kind of rise to the occasion and really try to think about 00:09:14.172 --> 00:09:19.193 the intersection between what they're expert in and what the Vis community might care about. 00:09:19.412 --> 00:09:25.852 And I feel like this was one of those where he had this sort of ternary triangular coordinate 00:09:25.852 --> 00:09:32.132 system where he thought about what it means to have naturalistic visual evidence, like. 00:09:32.481 --> 00:09:38.612 What Galileo actually drew as logs when he looked at the moons of Jupiter through the 00:09:38.612 --> 00:09:44.212 lens versus didactic purposes, which is what he put in the Starry Messenger book that's 00:09:44.212 --> 00:09:47.652 so famous, which is not a naturalistic picture at all. 00:09:47.652 --> 00:09:51.472 It's actually trying to specifically show things, and he talked about shadings of craters 00:09:51.472 --> 00:09:55.052 and the line of the sun versus evidential. 00:09:55.052 --> 00:10:00.352 And then he talked about how 40 years later, Hevalius came up with this full Selena Graffia 00:10:00.352 --> 00:10:04.892 book really trying to chart exactly what all is on the moon in terms of its craters. 00:10:04.892 --> 00:10:13.045 And I really liked this versus just a plain visual aid, which is just trying to help people see and think. 00:10:13.252 --> 00:10:20.512 And so I really appreciated the way he sort of unfolded these deeply historical things 00:10:20.512 --> 00:10:22.852 in a way that felt totally relevant for today. 00:10:23.145 --> 00:10:26.672 So I loved it. other people go to. 00:10:25.549 --> 00:10:34.399 Nice. And then what else? I think we have another keynote you wanted to talk about? 00:10:34.399 --> 00:10:41.399 Yeah, there was, I mean, a lot of the workshops have keynotes. And there were several that I liked quite a bit. 00:10:41.582 --> 00:10:49.399 You know, just in passing, Jean-Daniel Fiquette had one at the visual data analysis on scalability with progressive data science. 00:10:50.062 --> 00:10:54.059 And Sheila Carpendale had one, and Viz Guys on best practices considered harmful. 00:10:54.698 --> 00:10:58.559 But the one I want to go deeper into is the one from Kaseley Fiesler, which is at Believe, 00:10:58.559 --> 00:11:00.343 which is one of my very favorite workshops. 00:11:00.479 --> 00:11:02.990 It's all about methods and I'm a method geek. 00:11:03.199 --> 00:11:09.819 And her talk was called Data is People, Research Ethics Beyond Human Subjects. 00:11:09.819 --> 00:11:11.679 And it was just kind of great. 00:11:11.679 --> 00:11:20.679 Best title so far. Yeah. And it had some great taglines, like Twitter as the fruit fly of this kind of research, 00:11:20.679 --> 00:11:27.499 it's so easy for researchers to study. And it really went deep into, you know, there's 00:11:27.499 --> 00:11:31.419 all these sort of facile things you can say like, well, ethics is what the ethics boards 00:11:31.419 --> 00:11:36.577 makes you think about. No, wrong. That's not what ethics is. It goes deeper. And you know, 00:11:36.622 --> 00:11:40.979 if the data is public, everything's fine. No, wrong. Look at all these ways in which 00:11:40.979 --> 00:11:46.740 things can go to hell. So it was just a bunch of like, you might think it's so simple. No, 00:11:47.139 --> 00:11:52.459 is why it's more complex. I think one of the ideas that I hadn't quite heard before that 00:11:52.459 --> 00:11:57.174 I really liked is the idea of ethical debt, which is this analogy to technical debt where, 00:11:57.419 --> 00:12:03.459 you sort of don't deal and suddenly later it comes and whaps you upside the head. And 00:12:03.459 --> 00:12:07.659 so I liked that concept as a way to think about some of the ethical things of if you 00:12:07.659 --> 00:12:12.419 don't do them early, they will come to bite you later. Yeah. And I like the way she closed 00:12:12.419 --> 00:12:15.019 She said, critique the things you love. 00:12:15.019 --> 00:12:16.007 I love that. 00:12:16.070 --> 00:12:19.019 I love that. 00:12:19.248 --> 00:12:20.859 Yeah. Unfortunately, I missed it. 00:12:21.381 --> 00:12:24.640 But yeah, I want to see the recording. Yeah, definitely worth it. 00:12:24.838 --> 00:12:31.833 I think the idea that data is people is something that has been kind of like discussed some time before. 00:12:32.939 --> 00:12:42.859 I think it's something that database struggles with, right? the idea that you may have a visualization with lots of data points and it may be trivialized, 00:12:42.859 --> 00:12:46.651 but then every single data point is a person. That's so common and, 00:12:47.182 --> 00:12:52.763 and it's an interesting ethical dilemma there, Quite common in visualization, I would say. 00:12:53.807 --> 00:13:03.611 Yeah, and for ethical depth, I never heard of it, but it sounds, yeah, I can certainly relate when I think about projects I've been working on in the past. 00:13:03.980 --> 00:13:10.453 If you don't think about these things early enough, then when you realize that there is a problem, it's typically too late. 00:13:10.813 --> 00:13:19.797 Yeah, her exact definition was the implied cost of not considering social or ethical implications or harms now, assuming you can fix them after you find out what they are. 00:13:20.537 --> 00:13:25.162 Yeah. And of course, sometimes not too easy. Yeah, yeah, yeah, yeah. 00:13:26.137 --> 00:13:31.237 Totally. And did she give any specific guidelines? Or I don't know, does she have anything, 00:13:31.383 --> 00:13:35.452 that people can read to consider for their projects, something like that? 00:13:36.017 --> 00:13:42.737 Yeah, I mean, the whole talk was kind of full of lots of, especially a lot of concrete examples of what 00:13:42.737 --> 00:13:46.534 can go wrong when you assume the easy, simple thing. 00:13:47.097 --> 00:13:52.718 And I mean, she wrapped up with saying, questions to keep at the top of your mind are, 00:13:53.177 --> 00:13:55.932 what are the potential harms or negative impacts of the research? 00:13:56.197 --> 00:13:58.219 What can I do to mitigate these potential harms? 00:13:58.697 --> 00:14:01.757 Is there a benefit to the research and who does it benefit? 00:14:02.937 --> 00:14:08.562 And I think these are all useful questions. But basically this is another, 00:14:08.857 --> 00:14:12.307 take the 45 minutes of your life and just sit down and watch the video. It'll be well spent. 00:14:13.957 --> 00:14:18.978 I think a lot of what made the talk good was just the sheer number of concrete examples, 00:14:19.057 --> 00:14:24.109 that help people see how assuming the simple thing is probably not going to work out so well. 00:14:24.617 --> 00:14:25.153 Sounds great. 00:14:26.217 --> 00:14:31.537 And there were even more interesting keynotes as well that folks can check out for all the other workshops. 00:14:31.537 --> 00:14:36.577 For people that don't know, Viz has the first two days, the Sunday and Monday, are like eight parallel tracks. 00:14:36.577 --> 00:14:39.260 There's workshops and tutorials and symposia. 00:14:39.377 --> 00:14:44.184 So there's actually a, there's not just the main keynote. there's like eight more keynotes. 00:14:45.706 --> 00:14:50.513 So many of them are great. Yeah, because hosted event can have their own keynotes. 00:14:50.697 --> 00:14:53.657 And so why don't we talk about awards? 00:14:53.657 --> 00:14:59.965 There are also awards, there are many, and I think we want to focus on the 10 year, 00:15:00.298 --> 00:15:06.977 test of time award, which is an award that is given to papers that have been highly influential 00:15:06.977 --> 00:15:09.958 and highly cited in the last 10 years, correct? 00:15:10.813 --> 00:15:15.743 And there was one from VAST, and that's a paper that I've cited many times, which I 00:15:15.743 --> 00:15:20.544 like quite a bit, on enterprise data analysis and visualization and interview study. 00:15:20.985 --> 00:15:26.583 And it's Sean Condell and Andreas Papken, Joe Hellerstein and Jeff Hare. 00:15:26.583 --> 00:15:32.383 And it's basically like, let's go talk to people that are actually doing data analysis 00:15:32.383 --> 00:15:38.083 and viz in sort of business-y settings and find out what there is to know. 00:15:38.083 --> 00:15:43.203 And this, from a methods point of view, these interview studies where you are doing semi-structured 00:15:43.203 --> 00:15:47.623 interviews and you do qualitative data analysis to try to figure out what are the themes and 00:15:47.623 --> 00:15:54.443 what could you learn, they often precede many building visualization projects. 00:15:54.443 --> 00:16:00.403 And there was a time in Viz when it would have been hard to publish those when the only 00:16:00.403 --> 00:16:02.927 kind of work you could do is like, here's my new technique. 00:16:03.343 --> 00:16:06.943 And doing these kinds of studies to figure out, but who is doing what now? 00:16:06.943 --> 00:16:12.010 What are they doing? the pain points, what needs to be done, I think is super valuable. 00:16:12.263 --> 00:16:16.863 And so, you know, of course they went off and they ended up founding Trifacta, which 00:16:16.863 --> 00:16:23.023 is a nice success story spin-off. But you know, based on the kinds of insights you get when you go talk to people about what 00:16:23.023 --> 00:16:24.523 they're doing. 00:16:24.703 --> 00:16:28.142 So great paper, very happy to see you get the test of time. 00:16:28.484 --> 00:16:38.223 I agree. I think this paper has been very influential. for myself, kind of like give me the courage to work on similar studies and say, oh, we 00:16:38.223 --> 00:16:45.265 can do interview studies in our community and they can also be very successful. 00:16:45.583 --> 00:16:54.114 I agree with you. Interviews are so relevant if they're done well and if they're done with the right audience at the right time. 00:16:55.663 --> 00:17:05.963 I think now it's more common to see this type of studies. I agree because learning what kind of problems people have in practice or even the way people 00:17:05.963 --> 00:17:11.776 think about a problem in practice. I think it's really useful to inform. 00:17:12.740 --> 00:17:20.750 How we frame research, the practical research that we do on top of these problems or issues. 00:17:21.751 --> 00:17:26.315 Or when we learn more about what kind of problems people have or how they think about it. 00:17:27.026 --> 00:17:30.276 So I agree, very influential and very useful. 00:17:31.077 --> 00:17:34.552 And then we have another one that is one of your papers, Tamara, right? 00:17:34.705 --> 00:17:37.865 Yeah, that was pretty fantastic. 00:17:38.108 --> 00:17:43.870 Michael Saddlemyer and Mariah Meyer and I all did a paper on design study methodology at InfoViz 10 years ago 00:17:43.870 --> 00:17:48.236 And that one got the test of time this year So, 00:17:48.740 --> 00:17:52.750 You know in addition to sort of being personally gratifying like yay somebody cared. 00:17:54.670 --> 00:18:00.590 It's I feel like it's kind of a testament of like because this idea of design studies as a way to say, 00:18:01.010 --> 00:18:04.590 Let's do really really problem-driven research and try to you know valorize 00:18:04.590 --> 00:18:10.318 actually solving problems of people and and wait how do you do that like and you, 00:18:10.590 --> 00:18:14.910 know this paper came out of we did it wrong many times and I mean we also did 00:18:14.910 --> 00:18:20.190 it right many times but trying to figure out what you know if you're just sort of 00:18:20.190 --> 00:18:24.230 striding through the wilderness trying to figure out how do I even do this work 00:18:24.230 --> 00:18:28.790 how do I do it successfully how do I write a paper about it we ended up, 00:18:29.205 --> 00:18:36.790 coming up with a process that at least we found we were able to consistently get good results with. 00:18:37.037 --> 00:18:42.790 And I think one of the most useful parts of the paper is the giant list of 30-plus pitfalls of, 00:18:42.790 --> 00:18:47.790 don't do this, here's how to avoid it. And sometimes we did it wrong, 00:18:47.790 --> 00:18:52.790 sometimes we reviewed papers who did it wrong, sometimes we read published papers who did it wrong. 00:18:52.790 --> 00:18:57.830 It wrong. And so, you know, all the ways to get stuck, we wanted to help route people 00:18:57.830 --> 00:19:03.350 around those quagmires. And the nice thing is, there's just been this incredible explosion 00:19:03.350 --> 00:19:08.910 of work where there's a lot of really strong design studies these days, where, you know, 00:19:08.910 --> 00:19:12.750 and this is not the only methodology paper for it. There's a bunch of papers that, you 00:19:12.750 --> 00:19:16.790 know, continued on, some of from us, some from others, but the community has really 00:19:16.790 --> 00:19:21.910 done a whole lot of super strong work in these so-called design studies. And that's the part 00:19:21.910 --> 00:19:23.038 that's made us the happiest. 00:19:23.993 --> 00:19:31.523 Maybe for people who are not familiar with what a design study is or like how it's different from maybe describing a technique or whatever. 00:19:31.523 --> 00:19:35.039 Can you quickly summarize what the gist of it is? 00:19:35.237 --> 00:19:44.563 Yeah, so the gist of it is that you are doing, you're working on a real world problem and you're trying to solve that problem of real people with real data sets. 00:19:44.563 --> 00:19:48.483 So it's typically the standard approach is that it's very collaborative. 00:19:48.483 --> 00:19:52.563 Like you just can't do it on your own by theorizing. You have to actually talk to the people. 00:19:52.683 --> 00:19:59.523 Usually lots of trying to understand in the terms of another paper on the nested model, 00:19:59.723 --> 00:20:03.923 you're really trying to understand their abstractions. Take what they do and cast that 00:20:03.923 --> 00:20:08.483 into more abstract language so that then you can think about how you could build a visualization 00:20:08.483 --> 00:20:13.964 system to solve it. So a lot of work on understanding their task and data at an abstract level. 00:20:14.090 --> 00:20:19.363 And then it's inevitable that to actually build something, it's got to be iterative. You've got to 00:20:19.363 --> 00:20:25.363 to have rapid prototyping, really try many versions, check back in with the people. 00:20:25.363 --> 00:20:29.603 So there's often a kind of iterative development and deployment stage. 00:20:29.603 --> 00:20:34.323 And then often to try to figure out, so, hey, wait, did this work? 00:20:34.323 --> 00:20:38.523 You often really try to get people to use it in their daily work practice. 00:20:38.523 --> 00:20:42.883 It's a thing where you build it and then you watch what happens. 00:20:42.883 --> 00:20:46.760 Sometimes they use it the way you thought. Often they use it not the way you thought. 00:20:47.323 --> 00:20:51.683 Rather than trying to like run an empirical study that's a controlled experiment in a 00:20:51.683 --> 00:20:56.383 lab where you've got a little stopwatch telling people do this task, instead you're like, 00:20:56.623 --> 00:21:00.263 here, here is this tool, let me see what happens. 00:21:00.263 --> 00:21:04.563 And they're often verified through these case studies of like, well, first they tried it 00:21:04.563 --> 00:21:06.213 on something they already understood. 00:21:06.463 --> 00:21:09.303 And look, it took them 15 minutes to figure out something that maybe would have taken 00:21:09.303 --> 00:21:11.570 them 10 hours without this tool. 00:21:11.783 --> 00:21:15.703 And then they used it to do different things that they had not already anticipated. 00:21:15.703 --> 00:21:20.770 Often it's describing the kinds of things they could do with this visualization tool, 00:21:20.878 --> 00:21:25.010 that they would not have been able to do without it or that would have taken them much, much longer. 00:21:25.766 --> 00:21:29.916 So, you don't have to invent new techniques to do design studies. 00:21:30.312 --> 00:21:36.643 Yeah, that's really interesting because that's obviously close to the practitioners work, right? 00:21:36.643 --> 00:21:40.883 And like figuring out what people need and then building it and then seeing if it worked. 00:21:40.883 --> 00:21:45.283 And yeah, I think traditionally people might have said, well, that's just anecdotal if 00:21:45.283 --> 00:21:49.123 just do it once for a specific client and tailor something to their needs, you know, 00:21:49.123 --> 00:21:57.043 where's the big principle here? But I think your paper describes how you can generalize 00:21:57.043 --> 00:22:01.145 from single case explorations like this, right? 00:22:01.406 --> 00:22:03.323 Yeah, a big part of the paper is to say... 00:22:04.440 --> 00:22:10.770 You know what makes it research as opposed to simply excellent practice is the part where you reflect and you, 00:22:11.410 --> 00:22:15.009 You say did you confirm existing guidelines? Did you refute them? 00:22:15.810 --> 00:22:20.370 Do you need to propose refinements of them? Do you need to propose entirely new? 00:22:21.040 --> 00:22:25.250 Ideas on how to do this work. And so it's that what did you learn? 00:22:25.749 --> 00:22:33.290 That is different than what the research world already knew about in terms of guidelines is what I think is the part that makes it 00:22:33.290 --> 00:22:37.622 the research. But it's definitely true that a lot of practitioners very much follow these methods. 00:22:38.289 --> 00:22:42.050 Yeah. Yeah, but it's nice. It has so many facets to it. Like to a practitioner, it could 00:22:42.050 --> 00:22:47.050 be sort of the paper is almost like a guideline of how you could document your projects like, 00:22:47.282 --> 00:22:54.490 the how-to or this is how the project was done. It provides a chapter structure basically 00:22:54.490 --> 00:22:59.170 almost to that. So it has a lot of nice facets. Well deserved. 00:22:59.170 --> 00:23:00.450 Thank you. That's the final word. 00:23:01.073 --> 00:23:06.850 Yeah, I think one thing that I like about that paper is that it seems like, so you 00:23:06.850 --> 00:23:13.650 have a set of recommendations or pitfalls to avoid, right? And I like the way you 00:23:13.650 --> 00:23:17.810 describe it at the beginning saying, look, the three of us together, we have done 00:23:17.810 --> 00:23:23.770 like X numbers of these projects. So we have learned something about what all the 00:23:23.770 --> 00:23:26.046 mistakes that we have done. 00:23:26.110 --> 00:23:31.492 And I like this kind of premise. I think it's an interesting way to frame a paper. 00:23:33.050 --> 00:23:38.170 I just want to quickly mention about the awards. I wanted to highlight the fact that 00:23:38.170 --> 00:23:44.734 a lifetime achievement award has been awarded to Colin Ware, and this makes me really happy, 00:23:44.833 --> 00:23:51.729 because we don't talk about the work of Colin much, but I think he had a huge impact on visualization. 00:23:52.062 --> 00:23:57.890 So I'm really happy that he got this award. I think he really deserves it, especially not only 00:23:57.890 --> 00:24:01.010 for the excellent research work that he has done over the years, 00:24:01.524 --> 00:24:04.215 but of course for his super influential book. 00:24:04.330 --> 00:24:11.372 I think pretty much everyone here has gone through the book maybe multiple times. 00:24:11.444 --> 00:24:15.690 And yeah, I think it's really deserved. Yeah, and if you haven't, you should. 00:24:15.690 --> 00:24:20.356 Information visualization, perception for design, read it. It's still amazing. Yeah. 00:24:20.797 --> 00:24:24.659 I think it's what, the fourth or fifth edition now? Something. 00:24:24.970 --> 00:24:32.077 And I think it's the only book I can remember reading at the beginning of my PhD that now, 00:24:32.330 --> 00:24:35.399 is what, I don't know, many years ago. I don't want to know. 00:24:35.550 --> 00:24:40.440 And I'm still sifting through the same paper, the new edition, same book. 00:24:40.690 --> 00:24:45.617 So it's pretty remarkable. I think there are many gems scattered throughout the book. 00:24:47.462 --> 00:24:53.672 Yes. So I think we can maybe start talking about a few papers. Of course, this is a tiny, 00:24:53.672 --> 00:24:59.300 tiny portion of them. So Tamara, you wanted to highlight a few sessions maybe and maybe, 00:24:59.714 --> 00:25:01.551 specific papers within the sessions? 00:25:02.289 --> 00:25:05.792 Yeah. I mean, what I really want to do is talk about all the papers, but I can't because 00:25:05.792 --> 00:25:08.992 I haven't even read them all. 00:25:08.992 --> 00:25:14.992 And I will encourage for folks who did register online, there's this fabulous browse by serendipity 00:25:14.992 --> 00:25:19.152 where you can keep hitting the show me a new serendipitous set. 00:25:19.152 --> 00:25:27.392 And it's just a way to really get a sense of the breadth of the papers because there's hundreds and we are only covering a tiny few. 00:25:27.392 --> 00:25:30.512 Having said that, there was this session that made me super excited, 00:25:30.512 --> 00:25:33.365 which was on transforming tabular data and grammars. 00:25:33.797 --> 00:25:43.952 And there were several papers that I really liked in there. There was Animated Vega Light. So that was from Arvind Satyaranian's group. 00:25:43.952 --> 00:25:49.312 So first author Jonathan Zong and Josh Pollock and Dylan Wooten. 00:25:49.312 --> 00:25:54.892 And they had this really nice kind of framing abstraction where you think about time both 00:25:54.892 --> 00:26:00.592 as a way to visually encode data as an encoding channel, but also as an event stream and really 00:26:00.592 --> 00:26:03.792 reflecting on that dual nature. 00:26:03.792 --> 00:26:10.132 And so I found that to be a sort of interesting paper that, you know, for those who don't 00:26:10.132 --> 00:26:14.732 know VegaLite is sort of built on top of D3 and there have been a number of projects originally 00:26:14.732 --> 00:26:18.812 coming out of Jeff Harris Group and Arvind was a former PhD student with Jeff and is 00:26:18.812 --> 00:26:25.532 now an assistant professor at MIT. And so continuing this line of work of how it is 00:26:25.532 --> 00:26:30.132 that you can sort of think systematically about these things I think is really one of 00:26:30.132 --> 00:26:32.456 the strengths of visualization. 00:26:33.437 --> 00:26:39.767 Another one that I liked in the very same session was from Andrew McNutt on No One Grammar 00:26:39.767 --> 00:26:46.967 to Rule the Mall, a survey of static visualization DSLs, where DSL is the acronym for domain-specific languages. 00:26:46.967 --> 00:26:53.267 And it included, I mean, there's the paper itself which sort of goes deep into a nice 00:26:53.267 --> 00:26:59.534 analysis of all these grammars, but he's got this great accompanying site, you know, visjsonDSLs.netify.app, 00:27:00.327 --> 00:27:07.727 where you can actually start playing around with these hundreds of systems yourself. 00:27:07.727 --> 00:27:11.607 And so it's been more and more these days the thing in visualization that when there's 00:27:11.607 --> 00:27:16.558 a survey paper, people build these interactive faceted browsing sites underneath it that, 00:27:17.027 --> 00:27:22.931 lets you take all the data that they painstakingly gathered and browse it yourself for your purposes. 00:27:23.207 --> 00:27:27.847 And it's such a great thing that it's a thing in the field. This was not the only paper. 00:27:27.847 --> 00:27:29.386 There was someone like ML. 00:27:29.527 --> 00:27:33.212 There were several of these survey papers that let you browse underneath. 00:27:33.647 --> 00:27:38.433 Also he had a zine. He made a zine for this. I think somewhere there's also a survey of survey. 00:27:38.847 --> 00:27:40.909 Yeah, and there is even a survey of surveys. 00:27:41.467 --> 00:27:45.967 And Andrew actually made physical paper zines, one of which I have, which I love. 00:27:45.967 --> 00:27:50.847 He had a paper last year on zines and he didn't get to hand them out because it was virtual. 00:27:50.847 --> 00:27:53.967 So he brought that paper zine and this one. 00:27:54.673 --> 00:27:59.123 The other one that I really liked and that I noticed in my gigantic tweet stream has 00:27:59.123 --> 00:28:04.643 gotten just a huge amount of retweets and attention is this Hightailor system, interactive 00:28:04.643 --> 00:28:09.163 transformation and visualization for hierarchical tabular data. 00:28:09.163 --> 00:28:17.035 And so that's Goh Jung Lee, Runfen Li, Zhe Cheng Wang, Chi Harold Liu, Min Liu, and Guoran Wang. 00:28:17.283 --> 00:28:22.283 And what they did in this paper is they really, like, you know, everyone in their pet cat 00:28:22.283 --> 00:28:27.263 things about tabular data, right? Like tables, tables, tables. But they specifically thought 00:28:27.263 --> 00:28:31.823 about the fact that there's this hierarchical structure in tabular data in ways that you 00:28:31.823 --> 00:28:38.543 might really want to make use of and ponder. And so I liked their sort of system for thinking 00:28:38.543 --> 00:28:43.643 systematically about these hierarchical tables. So another great one. 00:28:43.979 --> 00:28:50.443 Nice. In general, I'm happy to see when there are new papers in this that talk about more 00:28:50.443 --> 00:28:55.268 about how to specify something rather than how to visualize something. 00:28:55.718 --> 00:29:00.603 I think understandably in visualization, we have a really strong focus on how do 00:29:00.603 --> 00:29:02.109 you visualize this, right? 00:29:02.443 --> 00:29:08.443 But we tend to forget that being able to specify what you want to visualize and 00:29:08.443 --> 00:29:10.698 how is at least equally important. 00:29:11.391 --> 00:29:15.532 And I think in general, I would love to see more research in that direction, 00:29:16.000 --> 00:29:24.003 because effectively somebody has to specify what they want to see and maybe also how they want to see it. 00:29:24.163 --> 00:29:29.962 And I think this is a huge... This is an area where we can make a lot of improvements, 00:29:30.376 --> 00:29:32.699 and being it specific... 00:29:33.194 --> 00:29:38.563 And it's something that gets reinvented all the time. All the time, yeah, exactly. 00:29:38.563 --> 00:29:39.963 Hundreds of grammars. 00:29:40.234 --> 00:29:43.934 Yes. This will keep going. I think that's the crazy part. 00:29:45.338 --> 00:29:53.107 But something like Vega-Lite really, or also ggplot really changed how everything is much more systematic. 00:29:53.413 --> 00:29:57.188 It seems to me like in describing visualizations than even five years ago. 00:29:57.188 --> 00:30:03.766 And I think the whole grammar trend has been super helpful in creating order. 00:30:04.188 --> 00:30:07.781 So, how we describe things. Yeah, totally. 00:30:08.188 --> 00:30:14.188 It's gonna be interesting how this develops. If there will be like one underlying, well, the paper says there is no one grammar. 00:30:14.188 --> 00:30:16.981 No one grammar to rule them all. The paper says, yeah, no one to rule them all. 00:30:17.368 --> 00:30:21.572 Come on, haven't you read Lord of the Wings? Maybe a handful of them, maybe, maybe. Who knows? 00:30:24.188 --> 00:30:26.155 Three grammars to bind them. 00:30:29.188 --> 00:30:33.188 Yeah, the real challenge is that you need something that is simple enough for a human, 00:30:33.293 --> 00:30:40.188 to create meaningful and expressive specifications, but at the same time it's readable by the machine. 00:30:40.188 --> 00:30:43.565 So I think that's what is really challenging there. 00:30:43.970 --> 00:30:47.388 Yeah. Who knows? Maybe it's going to be all natural language at some point. 00:30:47.388 --> 00:30:52.036 No, I'm not going to hold my breath. No. I don't think it will be. 00:30:52.423 --> 00:30:56.268 And if so, which language? Which language, right? Esperanto. 00:30:56.268 --> 00:31:02.268 There's more than English. Esperanto. There's your next year's paper, Esperanto for Whiz. 00:31:02.686 --> 00:31:03.748 There you go. 00:31:04.414 --> 00:31:08.264 Timing. Okay. So what's next? Well, there was another one. 00:31:08.264 --> 00:31:14.584 Speaking of people that do these kind of great websites underlying papers, you know, there's 00:31:14.584 --> 00:31:17.304 been this trend to have, you know, more and more supplemental materials. 00:31:17.304 --> 00:31:23.584 It's not just this like 10 page PDF. Benjamin Bach is always doing these great papers like this. 00:31:23.584 --> 00:31:30.984 And so this year's favorite dashboard design patterns, it was Benjamin and Ewan Freeman 00:31:30.984 --> 00:31:37.884 and Alfie Abdul-Rahman, Chetai Cherkay, Seiful Khan, Yilei Fan, and Min Chen. 00:31:37.884 --> 00:31:40.284 DashboardDesignPatterns.github.io. 00:31:40.441 --> 00:31:46.194 It's this thoughtful content incredibly beautifully and thoroughly presented. 00:31:47.670 --> 00:31:52.044 So they went through and they did all this analysis of dashboard designs and they have these 23 patterns 00:31:52.044 --> 00:31:56.884 that are hierarchically grouped into content and composition and these seven genres. 00:31:56.884 --> 00:32:06.304 And I think this is a paper that's gonna be super useful for a whole lot of practitioners and also advances the state of the art of treating 00:32:06.304 --> 00:32:08.104 dashboards as a research question. 00:32:08.537 --> 00:32:12.384 I feel like five years ago, dashboards were like the poor cousin and it was like, ah, 00:32:12.384 --> 00:32:17.315 dashboards. Don't ever say the word dashboard, right? 00:32:17.666 --> 00:32:24.057 The D word. And you know, more and more they're just, they're getting studied as things in and of themselves. 00:32:24.624 --> 00:32:28.104 You know, there was the dashboard conspiracy paper a few years ago. 00:32:28.104 --> 00:32:31.104 Yeah. What do we talk about when we talk about dashboards? 00:32:31.223 --> 00:32:34.860 And so this continues in that vein, and I thought it was a great paper. 00:32:35.104 --> 00:32:39.104 Yeah, and the site is, as you say, beautifully done and immediately useful. 00:32:39.104 --> 00:32:40.072 I can attest to that. 00:32:40.135 --> 00:32:46.104 Like, because it breaks down all the decisions you can make or all the variations you could do on a dashboard. 00:32:46.392 --> 00:32:50.020 And that's immediately helpful when you want to design one to think about. 00:32:50.686 --> 00:32:56.500 How many of these strategies to support or do I just take one of each in each category or are 00:32:56.500 --> 00:33:02.281 there different modes for it what what are the pros and cons of different approaches and as you. 00:33:03.060 --> 00:33:08.020 Say I like how they structure the content also so they do something that good dashboards also do and 00:33:08.020 --> 00:33:13.780 which they describe is to provide multiple like zoom levels and there's the cheat sheet and there's 00:33:13.780 --> 00:33:18.647 the quick summary and then there's a page for each important topic and then there's the full paper, 00:33:18.820 --> 00:33:22.300 And then there's the full table with all the dashboards they have been looked at 00:33:22.300 --> 00:33:24.940 and every single thing they have considered. 00:33:24.940 --> 00:33:28.420 And I think that's exemplary, really, really nice. 00:33:29.053 --> 00:33:34.340 Yeah, maybe old papers should be like that. It should be a new format. Who knows? 00:33:34.644 --> 00:33:41.580 I think it's a really good genre of paper to say, like, well, here's a domain of things that people do. 00:33:41.580 --> 00:33:46.380 Let's try and describe it and do some analysis on it. I think it's just awesome. 00:33:46.380 --> 00:33:52.580 I have to say that, yeah, I want to spend a few good words about the BIS community. 00:33:52.580 --> 00:34:01.740 I think that's been a strong trend over the years, trying to make research not only meaningful 00:34:01.740 --> 00:34:07.820 also for, say, practitioners or meaningful in the real world, but also translating the 00:34:07.820 --> 00:34:12.922 research in a way that people who are not writing papers can actually consume it. 00:34:13.100 --> 00:34:16.940 And I think it's been getting better and better over the years. 00:34:16.940 --> 00:34:21.978 And I'm really happy to see this trend going. I think it's very, very relevant. 00:34:23.022 --> 00:34:27.604 And yeah, who knows? Maybe data stories played a little role over there. 00:34:28.340 --> 00:34:30.900 I like to think that's the case. 00:34:30.900 --> 00:34:35.660 Or maybe also that medium publication that you have also really wanted. Exactly. 00:34:35.660 --> 00:34:41.100 I think it's a good trend. It's somewhat to blame for that trend. 00:34:41.100 --> 00:34:48.860 Yeah. Yeah, you know, there's a lot of open science and open data and there's even making the PDFs available publicly and what's, 00:34:49.100 --> 00:34:53.936 Changed dramatically in recent years is it went from only a few PDFs on archive to like, you know, 00:34:54.220 --> 00:35:01.580 Three-quarters of them on our archive in part because it's allowed and encouraged and described in the submission materials of how to do that, 00:35:02.245 --> 00:35:06.620 You know, there's more and more open data where people say, you know, not just the study results, 00:35:07.259 --> 00:35:16.560 But you know the the study stimuli let's document everything and code, analysis scripts. 00:35:16.560 --> 00:35:18.773 I think this trend is really super heartening. 00:35:19.565 --> 00:35:24.337 Yeah. Another thing that comes to mind, Tamara, I think that's true also for your book, that. 00:35:25.360 --> 00:35:31.360 It's clearly coming as an academic angle, but it has huge impact also for practitioners. 00:35:31.360 --> 00:35:36.720 I felt that maybe it's a good time to talk about your book series. 00:35:36.720 --> 00:35:43.600 I think it's another, I don't know how to say, something that is happening that I think 00:35:43.600 --> 00:35:48.526 can be described as things going between academia and... 00:35:49.183 --> 00:35:55.113 In the real world, so to speak, or practitioners, right? Maybe you want to spend a couple of words about that. 00:35:55.113 --> 00:35:57.276 I think that would be useful. 00:35:57.654 --> 00:36:05.353 Yeah, I would love to. So there's the book series. Alberto, Cairo, and I are co-editors, 00:36:05.423 --> 00:36:12.233 of the AK Peters visualization series, which is like a CRC route ledge imprint. 00:36:12.233 --> 00:36:15.913 And every year we have either a physical or a virtual table at Viz. 00:36:15.913 --> 00:36:18.710 So this year it was virtual. 00:36:19.287 --> 00:36:26.313 But hot off the presses are a few new ones coming out. So Neil Richards has a book, Questions in DataViz. 00:36:26.313 --> 00:36:29.558 He's somebody who comes out of this Heblot community, very much a practitioner, 00:36:29.913 --> 00:36:35.742 a lovely book of questions with some tentative answers and then even more questions, unpacking, 00:36:35.793 --> 00:36:39.370 all kinds of stuff about when to break the rules, when to go beyond standard chart types, 00:36:39.573 --> 00:36:41.063 what there is in life beyond a bar chart. 00:36:43.935 --> 00:36:48.753 And then there's this other one that's gonna be coming out within a month, I believe. 00:36:48.753 --> 00:36:53.293 Making with data. It's all about data physicalization. So physical designing 00:36:53.293 --> 00:36:57.413 craft in a data-driven world. It's this kind of all-star cast between the 00:36:57.413 --> 00:37:03.573 editors and the authors. So Samuel Huron and Till Nagel and Nora Ulberg and Wes 00:37:03.573 --> 00:37:09.053 Willett, and then a bazillion people that contributed. And even the book cover 00:37:09.053 --> 00:37:12.413 itself is a data physicalization of the structure of the book. So very meta 00:37:12.413 --> 00:37:17.513 because we all love meta. So super excited about that. I can't wait to get. 00:37:18.305 --> 00:37:27.313 My physical copy. And Nigel Holmes, we have a book from on joyful infographics. And he's 00:37:27.313 --> 00:37:31.593 someone who definitely comes out of the practitioner community, did a lot of extremely high impact 00:37:31.593 --> 00:37:39.913 work. And it's all about humor and joy. And Jen Christiansen is having a book coming out 00:37:39.913 --> 00:37:44.593 soon, Building Science Graphics. She's been very active in things like Scientific American 00:37:44.593 --> 00:37:52.473 and many, many other places. And Ali Torban actually did the covers to both Jen's book 00:37:52.473 --> 00:38:00.653 and Neil's book in a lovely circular way that the community all has ties to each other. 00:38:00.653 --> 00:38:03.713 We've got even more that are becoming out, but those are the ones that are coming out 00:38:03.713 --> 00:38:10.793 just in the next two to three months. So we are very, I mean, we want books from everyone. 00:38:10.793 --> 00:38:14.525 We want academics, we want practitioners, we want textbooks, we want beautiful books, 00:38:15.053 --> 00:38:19.746 we want everything in between, we want edited collections, we want single author manifestos. 00:38:20.373 --> 00:38:26.473 So if you want to write a book, we want to talk to you. Data sketches was in the series. 00:38:26.473 --> 00:38:32.453 That was a Shirley Wu and Nadi Bremer utterly fantastic one. 00:38:32.453 --> 00:38:38.793 Mobile data visualization came out recently, another all-star cast that grew out of a dog to a workshop. 00:38:39.938 --> 00:38:43.992 So I think books are great and we need more. I totally agree. 00:38:43.992 --> 00:38:46.600 Especially from such great people, you know. 00:38:46.761 --> 00:38:50.002 Richard Brath, Visualizing with Text, that's another favorite. 00:38:50.210 --> 00:38:51.488 Yeah, yeah, yeah. 00:38:51.848 --> 00:38:55.872 There was a visual analytics textbook from Christian Taminsky and Heidi Schuman, 00:38:56.268 --> 00:38:58.339 Interactive Visual Data Analysis. So... 00:38:59.986 --> 00:39:04.280 Maybe we need a separate episode just to talk about all these books. 00:39:04.451 --> 00:39:11.041 Okay, so what is next, Tamara? I got lost. Yeah. 00:39:11.112 --> 00:39:16.592 I mean, there's so many papers. We don't have time to really get into them, but I will just point out that probably particularly 00:39:16.592 --> 00:39:20.432 interested practitioners are making stuff go fast. 00:39:20.432 --> 00:39:26.952 So there was this Vega fusion paper from Nicholas Tristan, John Meason, Dom Moritz, and also 00:39:26.952 --> 00:39:33.792 Plotly resampler making large time series go fast and Plotly from Jonas and Jerwin van 00:39:33.792 --> 00:39:36.391 der Donk and Ernie L. de Prost and Sophie van Hook. 00:39:36.912 --> 00:39:40.912 So, like, just, you know, my jaw kind of dropped at the demos. 00:39:41.477 --> 00:39:44.912 I'm like, wait, that was really fast. That was a lot of data. 00:39:44.912 --> 00:39:47.912 How did you get some second response for that data set? 00:39:48.697 --> 00:39:55.912 Holy moly. So that was cool. And, well, I should, I mean, we also had some papers. 00:39:55.912 --> 00:39:58.699 I, of course, think they're fabulous and lovely. 00:39:58.912 --> 00:40:01.363 We worked on them. 00:40:02.120 --> 00:40:09.177 And actually both of them were originally published through through TVCG and then presented at Viz because that's a channel that a lot of work gets more, 00:40:09.832 --> 00:40:10.933 and more coming in. 00:40:11.140 --> 00:40:17.012 There's a, the Viz proceedings are a special issue of TVCG, but then also any TVCG papers 00:40:17.012 --> 00:40:20.192 on Viz that want to be can be presented at IEEE Viz. 00:40:20.192 --> 00:40:23.192 So there's a lovely relationship between journal and conference. 00:40:23.192 --> 00:40:26.992 So TVCG is the journal, right? Transactions on Visualization and Computer Graphics. 00:40:26.992 --> 00:40:31.992 Yeah, it's the journal. 00:40:29.820 --> 00:40:38.723 So we had one which was sort of fun with deep graph models or rather graph neural networks, GNNs. 00:40:39.038 --> 00:40:44.610 So our paper is called CORGi and it has various dog-related menu items. 00:40:44.610 --> 00:40:50.150 And corresponding a graph to its embedding is where we came up with that acronym. 00:40:50.150 --> 00:40:52.570 And so it's all about trying to see the... 00:40:52.757 --> 00:40:57.070 Basically, it's... You know how when you go on a car trip with the kids in the back of the seat and they keep saying, 00:40:57.123 --> 00:40:59.070 Are we there yet? Are we there yet? 00:40:59.070 --> 00:41:03.866 It's answering the are we there yet question for people trying to train these ML models, 00:41:04.181 --> 00:41:09.070 where you're like, has the model actually learned everything it's supposed to? 00:41:09.483 --> 00:41:13.070 Let's check, visually. So understanding the sort of topological structure 00:41:13.070 --> 00:41:15.070 of the original graph that supposedly 00:41:15.070 --> 00:41:19.070 is getting reflected in this, you know, high dimensional embedding in a latent space 00:41:19.070 --> 00:41:21.070 that you could then use to understand, 00:41:21.340 --> 00:41:25.070 some kind of topology space and this latent feature space 00:41:25.070 --> 00:41:30.190 beats on the graph itself. How do you actually see what the heck is going on? 00:41:30.765 --> 00:41:35.590 Because of course, as you might guess, it's complicated and you know, any of 00:41:35.590 --> 00:41:38.990 these super high dimensional spaces that you're doing ML on, it really helps to 00:41:38.990 --> 00:41:43.910 try to see what's going on. So that was a fun project. That was first author is 00:41:43.910 --> 00:41:49.350 Zipeng Liu. He's now assistant professor at Beihang in Beijing, China. Yang Wang, 00:41:49.350 --> 00:41:54.230 who started out at Uber and is now at Facebook. Bernard Yergin, who's now an 00:41:54.230 --> 00:42:01.530 assistant professor at Zurich and me. Yeah. So that was a fun to present. 00:42:01.040 --> 00:42:08.730 And we had another one which is called Gevit Rec. So data reconnaissance through recommendation 00:42:08.730 --> 00:42:14.810 using a domain-specific visualization prevalence design space. First on that was Anakrishan, 00:42:14.810 --> 00:42:20.330 who's now at Tableau Research, and Shana Fisher, who was an undergrad at UBC at the time, 00:42:20.458 --> 00:42:26.410 Jen Gardy, who at the time was at UBC School of Population and Public Health and BC Center 00:42:26.410 --> 00:42:33.770 Center for Disease Control, but is now working for the Gates Foundation on malaria stuff, and me. 00:42:33.770 --> 00:42:41.010 Ana's got a great blog post on the Tableau Engineering blog that's the super easy to 00:42:41.010 --> 00:42:45.350 access and think about version, and then of course there's the paper itself. 00:42:45.350 --> 00:42:49.742 We were happy to have those both presented to the world. 00:42:50.250 --> 00:42:55.290 Gevitrec is all about how is it that you could recommend automatically? 00:42:55.290 --> 00:42:58.924 Let's say someone flings a pile of datasets in your lap, what is going on? 00:42:59.290 --> 00:43:05.390 You don't want to spend hours, weeks, days, months kind of carefully designing a visualization. 00:43:05.390 --> 00:43:10.230 You just want to get a quick sense of what's there. So this world of automatic recommendation 00:43:10.230 --> 00:43:14.790 where given datasets, you try to figure out, well, how are they connected up? 00:43:14.790 --> 00:43:22.290 How could we help you see where they intersect and overlap? And then after you get that quick look, 00:43:22.290 --> 00:43:24.550 you could decide where you want to go deeper. 00:43:24.550 --> 00:43:30.297 But having the system that's built in R that just lets you type, show me what is there, 00:43:30.360 --> 00:43:34.930 and have that be something that you get in a second or two is super useful for this thing 00:43:34.930 --> 00:43:39.650 we call data reconnaissance of what is going on in these datasets. 00:43:40.488 --> 00:43:45.817 I like the term data reconnaissance. It really communicates what it is about. 00:43:46.438 --> 00:43:48.890 And yeah, there's been quite a few. 00:43:50.273 --> 00:43:54.283 Quite a few papers lately about this idea of recommending plots. 00:43:54.283 --> 00:43:59.883 And I agree that it's especially, it seems to be especially useful at the beginning. 00:43:59.883 --> 00:44:04.843 If you want to make sense of, you just want to know what's in here exactly. 00:44:04.956 --> 00:44:09.243 Right. And yeah, I like that. I think it's really useful. 00:44:11.545 --> 00:44:17.963 What is next? So there were 10 zillion other papers, but we should probably talk about some of the 00:44:17.963 --> 00:44:20.278 the other stuff as well, like maybe a few panels. Yeah. 00:44:21.323 --> 00:44:27.128 So one of the ones I liked a lot, so Vis in Practice is one of the workshops. 00:44:28.363 --> 00:44:34.023 That really tries to connect practitioners and researchers and often bring in a bunch of the folks 00:44:34.023 --> 00:44:35.842 who kind of straddle that line between. 00:44:36.338 --> 00:44:39.227 And so they had a whole panel on integrating research and products. 00:44:39.923 --> 00:44:43.483 And I actually, unfortunately didn't make it to the whole panel, but I came in like right 00:44:43.483 --> 00:44:50.203 as Richard Brath was giving this really fun talk about all the places where what you think you know what you're doing, 00:44:50.203 --> 00:44:52.524 if you just read the research, it just falls short. 00:44:53.001 --> 00:45:01.373 You know, things about 3D versus 2D, how many annotations could you cram into a single display. 00:45:01.625 --> 00:45:08.523 Just a lot of really nice concrete examples of where he found he had to sort of adapt and refine 00:45:08.523 --> 00:45:12.003 and not just take for granted the advice you get from the research community. 00:45:12.770 --> 00:45:21.853 So I liked that talk. Yeah, I think Richard has been in industry for many years, I guess, a lot in finance, 00:45:21.988 --> 00:45:22.834 or something like that. 00:45:23.323 --> 00:45:29.046 So yes, a lot of really... So at what used to be called Oculus, but it's now Uncharted. Yeah. 00:45:30.009 --> 00:45:39.539 Is the consultancy. And then as a researcher, I cared a lot about this panel on how is it that 00:45:39.539 --> 00:45:47.619 we review papers at IEEEV is and realizing that that might be a bit of insider pool for the non-researchers. 00:45:48.491 --> 00:45:52.579 But for those who are interested in the review process, which I do care about, because I'll be 00:45:52.579 --> 00:45:57.779 one of the overall papers chairs next year. So I suddenly care quite a bit about what's going on 00:45:57.779 --> 00:46:01.283 on the purview and thinking a lot about what various folks think. 00:46:01.778 --> 00:46:06.419 Um, lots of people, Cody Dunn, Alex Lex, Torsten Miller, Abita Otley, Melanie 00:46:06.419 --> 00:46:08.539 Tori were the speakers. 00:46:08.579 --> 00:46:11.590 It was organized by Bob Laramie and Petra Eisenberg and Tobias Eisenberg. 00:46:12.041 --> 00:46:17.379 Um, I'll be only, so I think if you are interested in those things, it's definitely 00:46:17.379 --> 00:46:19.909 worth, um, seeing some of that. 00:46:19.954 --> 00:46:29.819 The part I'm going to tell you is that Melanie Tori had the most fantastic extended metaphor about what kind of a dog you should adopt as a pet compared to what 00:46:29.819 --> 00:46:33.659 kind of paper types you are reviewing as a viz reviewer. 00:46:33.659 --> 00:46:38.459 So cute pictures of dogs, pointing out that you want the best of both worlds, that a lab 00:46:38.459 --> 00:46:43.699 doodle is both friendly and doesn't make you sneeze, and how does that connect up to 00:46:43.699 --> 00:46:45.691 the different kinds of hybrid paper types. 00:46:46.099 --> 00:46:55.499 It shouldn't just be a monolithic do this and nothing else. don't have to be like pure red dogs. They can be hybrids, they can be mutts. So I like that one. 00:46:56.899 --> 00:47:01.364 I like that. I sympathize because sometimes my students come to me and say, 00:47:01.742 --> 00:47:08.299 is this a technique paper or is a system paper? Is this? And I'm like, I don't know. 00:47:09.099 --> 00:47:15.259 Let's just do the work. But they also like the fact that there's some guidance. I think it helps 00:47:15.259 --> 00:47:22.744 helps kind of like reduce their anxiety. Fitting in a specific box, especially at the beginning, is useful. 00:47:23.259 --> 00:47:30.259 Yeah. I mean, guidance is always a trade-off. There's always this question of if you have no idea what to do, a path through the wilderness is great. 00:47:30.873 --> 00:47:36.259 Once you get more deep into things, you might find the guidance to be a bit more of a straight jacket. 00:47:36.259 --> 00:47:40.259 So I think guidance depends a lot on where the field is at. 00:47:40.259 --> 00:47:45.459 Types were, you know, in 2003, I'm like, we need paper types, because it shouldn't all 00:47:45.459 --> 00:47:50.759 just be technique papers. But it's 2022. You know, we already have these very fine-grained 00:47:50.759 --> 00:47:56.699 idea of contributions in the call for papers now. It's not just monolithic types. But 00:47:56.699 --> 00:48:01.199 it's still, it's good to know what they are. So you can break the rules deliberately knowing 00:48:01.199 --> 00:48:06.539 what the rules are. That was the thing Sheila talked about in her keynote at at Vizcom, 00:48:06.539 --> 00:48:10.547 which is you should know what the rules are so you know how and when to break them. 00:48:10.898 --> 00:48:20.659 How does that work practically? Like how do... Is there like any debate before the Vist Conference, like among the organizers about if there will 00:48:20.659 --> 00:48:21.340 be like... 00:48:22.349 --> 00:48:29.159 A change in what formats will be or should be part of the conference? Is there any curation 00:48:29.159 --> 00:48:34.772 on that sense going on? Or do you more react to, oh, we seem to be getting more papers that do, 00:48:34.919 --> 00:48:40.439 something we haven't seen before. How do we deal with that? Or is that not even an issue? 00:48:40.893 --> 00:48:46.439 AMT – Well, there was this giant spasm of introspection because the revised committee, 00:48:46.439 --> 00:48:51.239 which I was on for a while and then actually chaired for a while, was all about, should we 00:48:51.239 --> 00:48:56.089 integrate what used to be InfoViz, Visual Analytics, VAST, and SciViz together. 00:48:56.458 --> 00:49:01.103 So, as part of that whole revised work and of deciding exactly how to integrate them, 00:49:01.536 --> 00:49:05.239 we actually went super deep into this question of, well, what kinds of papers should there be 00:49:05.239 --> 00:49:06.784 and how could you define the contributions? 00:49:07.270 --> 00:49:14.085 So, we did actually think quite about that. We had a whole consultative process and we, you know, got the community together. 00:49:14.239 --> 00:49:19.239 So, as a community, we already had a whole lot of discussion about that kind of a few years ago, 00:49:19.239 --> 00:49:29.239 pre-pandemic, and then there is a group whose job it is to sort of analyze that data and look at it and see whether there are new ideas we should keep track of. 00:49:29.668 --> 00:49:45.142 We're probably not going to change instantly because we want to have a bit of stability, but we sort of tried to build into the new system this idea that you do go off and sync and analyze and try to understand where the field's going and be out ahead of it and not lag behind like a dinosaur. 00:49:45.239 --> 00:49:49.220 So the short answer is, yup, lots of thinking. 00:49:49.266 --> 00:49:52.794 As you say, if certain papers just don't fit certain categories, 00:49:52.857 --> 00:49:56.439 they might just not land as well. 00:49:56.439 --> 00:50:00.239 Well, this is why there's now this really fine-grained idea of contribution. 00:50:00.239 --> 00:50:05.425 It's not just like, here's six paper types. It's what kinds of things do you contribute? 00:50:05.802 --> 00:50:09.139 Did you propose a new technique? Did you propose a new abstraction? 00:50:09.139 --> 00:50:12.039 Are you providing a data set? 00:50:12.039 --> 00:50:15.912 So a lot of things that were sort of difficult to get academic credit for, 00:50:16.281 --> 00:50:22.565 you know, like providing open benchmark data sets or something you can really explicitly claim these days. 00:50:23.033 --> 00:50:26.787 So there is a lot more nuance there now. 00:50:27.318 --> 00:50:34.979 Mm-hmm. Yeah, that sounds like a good approach to think about the outcomes or the contribution, as you say, and not the packaging, basically. 00:50:35.807 --> 00:50:44.339 Yeah. I think you reminded me that that's the first episode we record about this, that they don't have to explain what is 00:50:44.339 --> 00:50:49.339 is size is vast and info-based and it feels really good. 00:50:50.697 --> 00:50:56.971 Yep. It's unified now, all kinds of work. There's now these multiple different areas. 00:50:57.347 --> 00:51:01.995 Most are not exactly any of the old categories, but mashups of many. 00:51:02.463 --> 00:51:07.819 Okay. I think you wanted to briefly talk about AltViz as well, Tamara? 00:51:08.152 --> 00:51:12.667 Yeah. AltViz is this new workshop. For those who know CHI, there's AltCHI, which is sort, 00:51:13.248 --> 00:51:18.523 of a bunch of unusual papers that don't fit the standard academic mold, speaking of paper, 00:51:18.907 --> 00:51:23.787 types. And so a lot of what they're looking for is what's provocative, what's interesting. 00:51:23.787 --> 00:51:28.290 You know, last year's ELF is had this great version of like heavy metal logos, which was, 00:51:28.587 --> 00:51:34.507 remarkably scholarly despite being hilarious. And so this year, my favorite ELF is paper 00:51:34.507 --> 00:51:40.027 is from Joe Wood and it's called Beyond the Walled Garden, a visual essay in five chapters. 00:51:40.027 --> 00:51:44.907 And it sort of fits into this theme that actually Marty Hurst had in the keynote, which is about, 00:51:45.080 --> 00:51:49.907 What do these new models allow you to do? 00:51:49.907 --> 00:51:55.947 I do love this sort of extended metaphor of the walled garden, where if you work in the 00:51:55.947 --> 00:51:59.879 walled garden, you feel protected and you know your place and you belong. 00:52:00.780 --> 00:52:08.227 But what could be beyond that? The early work in visualization with McKinley and Bertin was all about the atomization of 00:52:08.227 --> 00:52:13.419 design, reductionism, and really thinking about this question of visual variables. 00:52:13.547 --> 00:52:20.187 It, but what about expressivity and how this new technology could allow us to do things 00:52:20.187 --> 00:52:24.387 and how to even, you know, many people don't think of that as being visualization. They 00:52:24.387 --> 00:52:30.587 think about it as being graphics, but maybe not viz. Joe has actually made me rethink 00:52:30.587 --> 00:52:36.107 my hatred for Chernoff faces because of a point in that essay, which is interesting. 00:52:36.107 --> 00:52:41.287 I'm still not in love with, but at least I'm pondering. But his sort of central idea of 00:52:41.287 --> 00:52:47.287 Our walled garden remains home, but those distant lands beckon. It was a sort of poetic and evocative 00:52:47.287 --> 00:52:53.287 visual essay, which is very different from a standard research paper. 00:52:52.642 --> 00:52:58.092 This reminds me more of Moritz's Eurovis keynote, where he was also talking about. 00:52:59.375 --> 00:53:05.452 What we call this vis or not. I think that was inspiring. What is actually vis? 00:53:05.452 --> 00:53:12.812 Yeah, I think that's... Yeah, and also this anti-reductionism, minimalism, and it goes more for. 00:53:14.085 --> 00:53:20.837 Sensual and metaphoric and semantic maximalism that is often a bit, 00:53:21.134 --> 00:53:23.452 Undefined, but really interesting, right? 00:53:23.452 --> 00:53:28.452 And exactly, as you say, maybe it's outside of what we should even be looking at, or it's 00:53:28.452 --> 00:53:30.572 right at the core, you know, who knows? 00:53:30.721 --> 00:53:33.485 Who knows, yeah. So that makes it exciting, I guess. 00:53:34.502 --> 00:53:44.332 Yeah. That reminds me also of this effective, this taxonomy of effective data visualization. 00:53:44.332 --> 00:53:48.212 Oh, affective, yeah, it was one of the best papers. 00:53:48.672 --> 00:53:53.212 Yeah, yeah. Not effective, but affective. There was another cross connection to even look at, 00:53:53.212 --> 00:53:59.799 what are non-analytic goals of data visualizations that are more on the effective or emotional level, 00:54:00.609 --> 00:54:05.212 and how could we characterize those, what's a good language to talk about those. 00:54:05.443 --> 00:54:10.133 I thought that was also very timely on that whole arc. 00:54:10.439 --> 00:54:13.212 And that was a paper from several folks, including Lais Padilla. 00:54:13.500 --> 00:54:16.003 Who's that you seem to have said? 00:54:16.372 --> 00:54:20.918 Okay, so I couldn't identify any specific trend this year. 00:54:22.452 --> 00:54:27.132 Any other closing thoughts or maybe? Yeah, I think the goofiest two things I saw 00:54:27.132 --> 00:54:34.772 in Oklahoma City were that there's this human-made canal and if you keep walking and you go way out 00:54:34.772 --> 00:54:35.844 and get a little bit lost, 00:54:36.177 --> 00:54:42.852 there's this part where there's giant horse sculptures that go diving into the canal and then coming back out. 00:54:42.852 --> 00:54:46.313 And they're like three times life size And it's quite surreal. 00:54:46.502 --> 00:54:49.086 What is going on with the giant cavalcade of horses? 00:54:50.792 --> 00:54:54.852 And then they have a skeleton museum with hundreds and hundreds of animal skeletons, 00:54:54.852 --> 00:54:59.556 which I had fun visiting on Friday, skeletonmuseum.com. 00:54:59.872 --> 00:55:02.841 It's well worth your time if you're in Oklahoma City. 00:55:03.994 --> 00:55:11.889 Yes. Okay, well, I think that's it for today. Thanks so much, Tamara. 00:55:12.114 --> 00:55:17.072 Thanks for helping us go through lots of exciting new research. 00:55:17.072 --> 00:55:18.072 Thanks for having me. 00:55:18.560 --> 00:55:25.797 It's always fun to have you on. So yeah, we'll try and collect all the PDFs and all the talks. 00:55:26.301 --> 00:55:28.072 Some of the talks should be up on YouTube. 00:55:29.092 --> 00:55:34.072 Yeah. Hopefully you'll find a way to follow up on all that. Make sure to go to the show notes. 00:55:34.072 --> 00:55:37.788 Show notes that would be plenty of links over there. 00:55:38.698 --> 00:55:43.508 Okay, thank you. All right, and see everyone in Melbourne. Viz 2023, Melbourne, Australia. 00:55:43.508 --> 00:55:47.588 Yes, we forgot to mention that. Yep, see you in Melbourne next year. 00:55:48.996 --> 00:55:51.028 Bye-bye. All right, bye-bye. 00:55:56.387 --> 00:56:01.086 Hey folks, thanks for listening to Data Stories again. 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