 It's still our close answer for this meeting on psychotic. Professor Kim is both a theoretician, an experimentalist, and a modeler. What better to have as your first keynote speaker? One second. Thank you. I'm once again at the University of Texas-Alston. I'm very pleased to be here, representing the S.A.N. team led by Brandon McLeod, Kim Boreal, Miller, Robin Martin, and Leslie Sheen. S.A.N. is a sediment miscellaneous network. We are very happy to help hold this meeting, joining the CSUNS. Thanks to the CSUNS, allow us to talk about participating here and the researchers. I'm pretty sure this meeting will be a starting point to have a great synergy between the two committees and also a lot of strong collaborations between the two committees as well. S.A.N. means strong in Korea. And you, in Turkish, I present this at 8 o'clock. And everybody loves it, so I just need to use it again. And it means we want to build a strong community for you, experimentalists and moderators. S.A.N. presents Sediments Experimentalists Network and this NSF-sponsored EarthCube Research Organization Network projects. We aim to support our data-enabled community for its diverse research process. We have three different focus groups. First one is ECM, this experimental collaboratories. The second one is ED, it's education data standards. And third one is KB, it's non-space. I want to go on further more details about individual focus groups. First one is experimental collaboratories. We want to enhance more collaborations between the laboratories. We are always working along. So even though we come to the Asia meetings and other international meetings, we talk with each other, share the ideas, but we never really cooperate with each other between the labs. So we want to enhance more collaborations. To do so, we want to develop some insight structures to facilitate that. And we want to broadcast experiments and also put together some community-driven challenges together. Here is the screenshots, one of the YouTube live video of one of the summer institutes experiments in Kyle Strauss' location. And he also has a live experimental calendar that anybody can sign up and just let other people know when you are conducting experiments. We broadcast through the YouTube of the experiments that we watch in your desktop. The second one is non-space. We want to develop online resources for experimental data management. One of the major thing is set-ups.med, which is this, the screenshot. If you go to this website and browse into the data or set-ups, this is one of the window that you can see. There are many entries already. We, this is a forum for the user-based information to change and also it has a lot of metadata, practices and methods and laboratories information. We so far have 45 data entries, 26 set-ups, 18 methods and 21 treatments and six labs. So I'm continuously getting the questions from other people. How do you make sediment either? How do you control the room? Where do you buy sediments? There's a no common web-based resources we can go and find. We want to serve that to the community through this set-up. Third one is EC. We want to develop and disseminate recommendations for the data practice and standards. One of the major outcomes we have done, we publish the paper based on the inputs from the community and discussion through the many workshops and town hall meetings. We have originally published it in the filmology, in the enhanced and simple room, special volume. You can find a lot of community challenges, strategies and scientific opportunities in the paper. We have done many workshops, including this one, this is our four workshop. First one held in UTS in way of 2012. And 2013, we went to Japan in Nagasaki University. And 2014, we went to Netherlands, Utrecht University, and had a workshop there. We nearly worked hard to gain more understanding what we need for the data management and how we share the data with the community. We also hold the AGU town hall meetings between 2012 and 2014, using the inputs and discussions and insight we gained so far for the last three years. We went to the short courses like the summer institutes and continuously educating young researchers. And we want to do continuously about doing that. In this EC education and data standards, there are two most significant challenges. One is data discoverability and second is data accessibility, something I'm going to talk about more. Because I'm so proud of all the workshops including this one, I've got some pictures. The first one at UT, we conducted experiments together. We made a survey, you know, what the participants wanted to conduct. And based on that, we changed the parameters and conducted experiments and tried to share through the web-based webmaster. We had discussions, the participants participating in the small experiments, we had a lot of fun. The second one in Japan, Slapped to Dynamics was the main, Patsy Mimuto and Patsy Melanica was the one who made this possible. And Brandon McLoey, one of our PI, he works so hard between the workshop base to the hotel. We need to use the train, the small train to get there. He always just fell in a sleep because we worked so hard. This is the picture of we mixed the swimming water to make it to be decreased. The third one, we went to the European country to make better figure out community including European researchers. U.S. was the one who made this possible. We went and conducted experiments in a Euro tank, made a fairly intense discussion in bright outlooks we learned about in the community. So we worked hard and we've done a lot of things. But I didn't say why we need a community, a network like a sedimentary experimental lab at the end. I just want to briefly talk about data challenges in our community. I know a few terms, new terms through these projects. First one is data, you still have your massive data and it's not that good. You still don't know how to do it, how to share it through the web, to share it with other people. Still you sense by email, your hard drive to the different person. So, doc data represents, there's no accessible in the computer internet, but it's either way. Second one is a quick data. In my laboratory with my students, we generate our image data because we take the images in our experiments. We generate a ton of data easily in a one single experiment. And it takes a lot of space in there. It's in a hard drive of course. So managing this big data is a problem and this is a challenge for our community. Also, search through the exact data set or exact data points we want to find in a big data resource without the change. Third one is diverse data. This, our community, probably including the modeling community, is an example community for the long-term data, which represents one of our kind, the experimental setup, and also the isolates, the experiments. I briefly mentioned about, even between the experimentalists, our communication is quite known. We have a lack of communications. We don't know, in other experimental facilities, what kind of sediment heaters are used, what kind of grains are used, whether it is rounds or shapes. We actually don't know exactly how we do produce other experiments in my laboratory. So, quite a long tail, as you see here. And then our final one is a separable layer. The funding agency keep asking you about data management problems. You need to prepare for that. And also, continuously, John knows our asking. So, your data associated with your paper is available online online. Have we ready for that? We already, are we ready for that? Do we have the tools or channels to resolve these problems? Here is the solution. What are the solutions? How do we come to the clinic to bring you on? The same provider clinic. Take only measurements and leave only data. You will find a lot of insight that you have gained for the last many years. And you will get this bucket. So, we're gonna talk about best practices for data collection and manuals. We're gonna talk about the life cycle somewhere there. Metadata, let me think, is the best or optimal for it right now. Data preservation, discovery. And we use, and we also make, talk about the workflow and cyber infrastructures like our non-space and seed and geosynetics and how we just use those to make data we pass to it. So, please, this is a short time period, so I cannot tell you all the quick things we found, but if you come to the clinic, you will find. Speak to the viewers a little bit. I want to talk about society. The scientific challenges. The same does not want to only give you a tool, two names and data. We want to drive ourselves using great science. I think we want to acquaint the new culture to share the data and the use and discover the existing data, but the best way to carry out is enhance our community, support our community to do the best science. So, I've got some grand challenges with them, and one of the great outcomes from the workshops in Nadasaki University that you probably recognize this person. It's Gary Parker. He helped me dance at the meeting along the Korean song, where he was playing, and then he showed us a great vision about EarthScape 2100. This is actually linked to the current theme for the CSNAC, so the imprints of climate change on the landscape and seascape. I'm going to give you a few examples of my own experiments and dealing with our own to branch out in our expander community about repeatability, stateability, and also autogenic and autogenic processes to touch on this big picture. Topic. So this is one of the very small experiments done in less than one meter long and 50 centimeter wide. But we did this. We want to simulate the Arctic Delta. So, I brought a small ice machine, ice brush machine, and then students just put a few dyes and crushed it and put it into a small flume and packed it and make it ice-covered. And then put a water and brings the sediments from the one corner and building a delta. Since the ice is okay, you cannot see through it. We put a camera underneath of the flume and watch that happens. On the left-hand side, this is no ice condition. So you can actually see, you'll be switching, but very frequently, and make a very smooth progression to the old directions. However, when we run with the ice cover, about five to seven centimeter, and run an autogenic experience, you actually see pulses of delta-2 propagation. There was a channel initiated by the inaction between the ice and the water and pushing the sediments into the one location and switching to the different location. So it caused the very rough roadways, the delta-2 line. After we drain the water, this is the picture from the top. On the right-hand corner, at the center is the sediment source, and this is a shoreline in blue, and then you can actually see the top side and foreside. Especially foreside, you could see, using the ice cover, it probably negated to the downward, and also has a lot of rough topography. Just putting the ice, and let's bring the very similar experiments. However, it caused a very strong difference on the topography development. I was very excited about it. It was so oddness when I looked at the topography, after we melt the ice and looked at the topography, what happens was so clear to me. Because we oftentimes isolate one single parameter to address in the experiments, and we also access, in the time scale and the spatial scale, we cannot access in the natural system. So activation of the core processes through the experiments is easy, and it produced a high-regulation data as poor ideas. So the channel underneath the ice was very obvious, we could generate a conceptual model about what happens in the experiments. However, if I write this paper with this data, and then once published it, maybe you were one of the reviewers, and say, okay, how do you store it? Do you have any natural example? It's absolutely the hurdle we have. All reviewers have this model. How we can resolve this? In this particular experiments, we luckily have some experiments, the natural data, you know, R2-Δ, and a mis-deliver, shows a pie-difference on the surface of the force set. So kind of like it depends on what we are presenting. However, most of the experiments and their data and their research have a big problem of our scale. Here's one example. I wrote a few papers using the experiments, but there's a pattern. Whenever I submit a paper, the reviewers say scale, scale, scale. But this particular one, I didn't get any. Why? This is something I want to tell you, and I need my readers' help. On the left-hand side, it's just a very small tank, and one-dimensional, very narrow, and perfect sediments, but mixture, to create a building up. I used a corksand, which are proxy for the coarse sediments, and warm-up pressures for sediments, proxy for fine sediments. You can actually see the boundary between the white ones and brown ones. There's a shoreline. The silver one's rising very slowly. This boundary between the coarse sediments and fine sediments is proving around. It's just following how the shoreline is proving around. However, on the right-hand side, I made a little bit of a higher civilized rate and see what happens. So on the right-hand side, the white ones, brown ones, would start to show very similar pattern. However, over time, it started to retreat. Grainside changes in between the coarse, natural, and the fine natural was really start to retreat. However, doing that, this shoreline is still proving around. Okay? I made a set of experiments with different civilized rate and see how the pattern changes. There was other grainsides and proxy for the coarse, of course, sediments and fine sediments and the natural systems always keep the same sediment supply and water supply isolated experiments parameters. But then we look at the one single column. In this particular paper, I wrote a post. Maybe I just stole a couple things from various Hebrew chapters and combined them to have accelerations, the sediment mass values to calculate the sediments at transport and evolved the surface, but have three different moving boundaries. First one is gravel sand transition, which is E, and shoreline, which is the downstream boundary, and the delts itself. They're moving with each other in these parts of the sea of the rice. However, I used the well-known semi-transparent relations for the gravel and sands for copper 1979 and also angle on the hands in 1972. I ran a couple of tests, tests modeling with a two millimeter per year and civilized six and 10 millimeter and 10 millimeter in this particular case caused a retreat, faster retreats of the grain size transition compared to the shoreline. Shoreline is still pivoting out, but in terms of the mass balance, the first material leaves the mass when it starts to retreat. So it caused a very similar patterns between the modeling and the experiment. Okay, and then what I did is, oh, so this is the right why it happens. In terms of the geometry, if I just cut this in focus, the sea of the rice is better much. Because of the process flow, grain size transition actually has additional sea of the rice. So it responds to the faster sea of the rice starting to be much earlier. Transitional coagulation policy concept shows coagulation, uniform, but that might not be correct because in this experiment. This is a trick that I made to scale and didn't get any viewpoints of the scale of the experiment. I calculated the slope in the sediment transfer empirical relationship in the experiment and then plug that empirical relationship into the logistic model. We placed the caucus at all, 1979, and angle enhancer. And it produced exactly the same patterns like this. So it captures both the natural system and an experimental system. I could do very simple modeling, but you can do much better, right? Coming back to this, to explain to you, remember, this is the ultrasonic processes. I can talk more about this ultrasonic processes, but I want to emphasize, it shows the systematic changes, the magnitudes and differences are changing by signalized rates. If the signalized rate is high, you have a smaller standard deviation, a small cause, large standard deviation. So it works as a signature. We thought ultrasonic processes and its structural patterns was coming from noise. But I don't think it is from the noise. We need to understand this. Models need to model internal dynamics and stress graphic signatures to understand the climate changes and the weather. So I view ultrasonic as a climate change and ultrasonic as a weather. We have a lot of opportunities to understand the systems, sedimentary systems, using ultrasonic processes. My prep shows eight minutes here. So this is the conclusion. So we have granted this in our community. We need to know how to be produced, someone else's experiments, and we need to know how to scale our experiments. We need to know how to understand ultrasonic signatures and allergenic signatures. Using any experiments, among the experimentalists may take longer. However, if we make a joint effort, modelers and experimentalists will work together and probably can advance our science much, much in the best way. You saw the examples, but this is only few in the solutions we have in our community. You probably already have a much better ideas in your head already. So joint efforts, thank you very much. I think our phone needs to be turned on. So questions, who would like to go first? We have questions and mics. Just stand up, go to one of those mics and share our answer. Kim or anyone from the experimentalist may ask a question. Mary? Hi, really nice talk. So from a modeling viewpoint, what I emphasize a lot with people is sensitivity analysis. And that really simple models, you can kind of understand the dynamics. Some of the things get complicated, you can't. And so I was just going to mention that in some of the modeling we do, showing how things match is great. And that digging into why we met, what was important? What did I, did I have to change anything? Did I decalibrate? And what really dominated that is really just, to me at least in it, I just mentioned in connection with your work as well. Thank you. And we often actually come into the complicated problem first. So, but what I've learned so far, whenever I want to copy the nature, I fail. But if I know how to simplify it, I kind of learn the things. And from the simplifying things, we can actually step. And based on the basic findings, we can add more complicated things. So that I think would be one of the good structures. Another question. So let's, I'll ask Rudd to continue. So, so, you know, the sediment experimentalists, they are, they provide the appearance at least, things worried about scaling. And you mentioned it. But why do you think that is? Actually, personally, I don't know why. Because if I use computer model, then I think at home, I talk one more time whether it is real or not. But if I run water and sediment, this is the real one, isn't it? We're doing a science. But I know why the readers and also the community are concerned about it, because we are doing science. We want to apply our insight into the natural system. And we want to understand Earth, right? So I think it is a valuable point to know how to scale. But however, I always think that we learn from the lab at the tree experiments. It has its own meaning. And it's based on the physics. And sometimes we corroborate the words like a chemistry, right? So it's, again, it's a nature. And I think it's one of the small scale. Yeah, because I'm not in your community, but I wouldn't be too worried about the problems. But, you know, because modelers scale all the time, in fact, if it's a good model, it's already scaled. So scaling is just the very nature of how we do our business in the modeling world. It should be the very nature of how you do your business. So I shouldn't be so sensitive. But Tom. Where do you think we progress? I think there's a lot of people that have been worried about scale. But I think that if you scale people wrong, you're going to need some of your content. I'll forget what the rules are for you. I agree. But for a lot of the people who follow you, I think that you have a lot of talent in the modeling world. And there are a lot of people who follow you. And there's a lot of talent in the modeling world. Sure. What is this great point? Question, yeah? Yeah, I think that there's a lot of community about, like, I work more on the very small scale of processes. And I think that should connect more to be a small experiment, because we give this challenge, you know? So for example, if somebody do a very small experiment on a small house, they should run on trash in the city and stuff like that. That should kind of make a community cry, because then it's like, well, maybe we should die a temperature of this or a content of sand versus ice that way to have a more systematic approach in the sense of getting the sensitivity to it. Or, OK, very, this specific thing I know that's very, very dangerous in this kind of point. And maybe there's health to go in there. I understand that you can connect to many, many experiments when you have a complicated system, but if you know you just want to do great points, if you're the first high school member, do you know that that's the scale which matters the most? They know the scale. Got us. That's it. Enjoy our discussion. How do we convince more people to take their data from the dark side to the good side or the light side? So this is one of the major things we have worked on. We need credit, right? But sometimes you try to publish it with the data, and you fail. It's like a scaling problem. But still, the data will be useful. Maybe someone else will be useful. But it should be discoverable, right? So, for example, there are seeds. And that's what our support is. Now, it's not a data repository. It can hold your data. And also, once you publish it, you get newer. So it's sightable. In that way, you can get something. And you can send a bit of data and you can speak about this. We would like to create experimentalist data publication. So only the data, it's a fine data with a good metadata, can publish through that channel and get our citations and so on. So the authors can credit part of it. So I think this is one of the ways we can increase the users of experimentalists to make their top data, you know, right data. Right data? I don't know what I'm talking about. So let's take one second one first time. All right. All right.