This is an official live stream of the CNCF and as such is subject to the CNCF code of conduct Please do not add anything to the chat or questions that would be in violation of that code of conduct Basically, please be respectful of all your fellow participants and presenters and with that out of the way Welcome to CNC face-off where everything is made up and the pods don't matter and now Let me introduce your hosts the face of DevOps the thought leader of thought leaders The as always incredibly luscious the one and only Maddie Stratton Here we are This is when the inspiring theme music would play and we always have something to improve on our next episode So now we know what it is. I want theme music is what it is But yes, welcome to CNC face-off round two if you missed two weeks ago We had round one where the rogues gallery faced off against contrabacks and rogues gallery We're the winner moving on to the semi-finals We've got two great teams facing off against each other today and the winner of this face-off is who will face off Against the rogues gallery in the first round of the semi-finals in a few weeks But let's go ahead and get started and meet our teams Our first team is team tinkerbell who is led by David. Welcome to the show David Hi there, thanks for having us. My name is David Flanagan You may know me as raw code and I'm a developer advocate for a cloud native Kubernetes amber metal Joining team tinkerbell is Markey Hello, my name is Markey. Everybody knows me now as Marty. So please feel free to call me Marty and I also work at equinix and Rounding out team tinkerbell is Jeremy Hey there, I'm penguin on the internet Jeremy to my mother and the law Learn more about tinkerbell at tinkerbell.org Excellent. What a great team and then facing off against them. How many times can I say face-off in the same show? I need some new words is team dev oops, which is captain by Brendan Yeah captain's a strong word, but if it's dev oops, I think that that works. Thanks for having me, man Yes joining a Brandon is Anise My name is Anise and I work as Set reliability engineer at Sevo and I started a challenge called 100 days of Kivanita. So check that out It is an amazing thing for you to check out. Everybody go check that out and rounding out team dev oops is Dinesh Hi everyone. My name is Dinesh. I am director of innovation at Sevo Excellent. All right. So the way this game is going to work is we have surveyed hundreds of dev ops cloud cloud native engineers to get their answers to some survey questions and our teams are going to face off to try To see if they can guess what the most popular answers were and we're gonna start with our first face-off with the two captains against each other so if we can get David versus Brendan get your hands on your buzzers What will happen here is the first one to buzz in with an answer that's on the board may have the opportunity To play the round. So with that, let's go ahead and see our first question Which is what is the main benefit you get from kubernetes? And that would be Rocco David David. What is the main benefit? What's the top answer on the board? crash-loop back-offs Show me crash-loop back-offs Paul No, that is not it. Okay. So Brendan Brendan because of that miss then That means that team dev oobs gets to play. So we'll go ahead and start with Brendan. So Brendan We're gonna go one person at a time and see if you can guess the top answers on the board for what our Hundreds of engineers would have replied as what is the main benefit that you get from kubernetes? I'm gonna go with YAML based Development show me YAML based development Abstractions we'll take it. We'll take it. Okay, moving on to an ace. What? Based on our survey. What is the main benefit you get from kubernetes? You always have an answer to your manager. You always have something to do. You're always busy. I'm always busy Show me justifying employment Number one answer on the board excellent, okay Team dev oobs is off to a great start. Okay, Dinesh. What's the main benefit you get from kubernetes? complexity definitely Survey says pain Okay. All right, we'll loop back again to Brendan Brendan We surveyed hundreds of developers and what did they say is the main benefit for kubernetes? I'm gonna go off the board here. I'm gonna go serverless is somehow the benefit Show me something with serverless No first strike now remember if team dev oobs gets three strikes then tinkerbell will have a chance to steal so on ace We got three more answers on the board two strikes to go Give me an answer containers Show me containers Survey says No We got one strike Dinesh. Can you make it happen? I don't know it's Maybe yamal I thought we had something with yamal already. You might I'll give you a I could be rocker. Show me darker Ah, okay, so here we go now team tinkerbell. This is your chance to steal So amongst yourselves you will get one answer and if that answer is on the board you will get the points if it's not Then team dev oops will get the points. So David consult amongst your team and then let me know and then come up with your one answer that you're gonna you're gonna posture postulate All right, what we're thinking I'm thinking web scale I was gonna think scale. It's either the scale or orchestration What do you want to go with I'll let you be there to say that marky Hmm Yeah, okay, let's do orchestration Show me orchestration No So that means the points will go to team dev oops And but uh, let's see what the remaining answers were on the board Number six answer was scaling. Oh Oh marky next answer Content for twitter shitposting And the final answer on the board is Software deployment. All right, so that's gonna move us on to our next round Uh, it's fine. Don't worry tinkerbell. There's still still plenty of time to go And this is gonna be now marky facing off against on east. So players Get your hands on the buzzers. Are you ready? Okay. Oh wait, let me clear rock out of there. There we go. And our next question is Something someone would find installed on your phone And an ace The board is yours Something someone would find installed on your phone a dictionary Show me a dictionary No No All right marky. This is your chance to steal control something someone would find installed on your phone Candy crush Show me candy crush Games okay, so dev oops will take the board Uh, jeremy you are up first. Uh, why don't you uh, what's it? What's the answer to something someone would find installed on your phone?  Give me to show me twitter show me twitter Twitter number two answer on the board. Okay, david We surveyed hundreds of developers and engineers and asked them what's something someone would find installed on their phone read it Show me read it. Oh, okay. That's one strike marky still got two strikes to work with but a lot of answers to come up with Something someone would find installed on your phone What is vpn? Show me vpn Two strikes a lot of pressure jeremy a lot of pressure Come on a lot of board to clear with only one strike to go Uh, what so more than uh, how about slack show me slack Survey says messaging collaboration apps number one answer on the board. All right david still got four to clear Uh, this is where it gets tough Something someone might find installed on your phone Uh, it shouldn't be less difficult email Well, we have messaging collaboration apps. I'm giving you a chance to maybe All right Show me pager duty Pulled it out. Okay marky got a couple more to go. What do you got here? Dating app show me a dating app. All right team debuts your chance to steal There's three answers remaining on the board brendan What do you think Yeah, absolutely. Yeah something related to pictures now like yeah, maybe picture editing instagram, but What about spotify? Spotify music Yeah, I'm just thinking yeah, I mean something also like exercise like nike ron or But music music is also. Yeah, I'm gonna do it's a safer bet music now. Yeah Oh, let's go for music. Okay. Is that your answer? Show me music apps No Pinkerbell will take the points But I'd like to clarify that I actually do not have dating apps installed on my phone. I just want to My partner poked her head. It was like No, no, no All right judges, let's see what the remaining answers on the board were Unused productivity apps malware and the final answer was A password manager. Okay, so this is a close game two points is not a lot So everything is still up for grabs with two more rounds to go in the face off this next round We've got uh, Jeremy against Dinesh so Players are you ready? Hands on buzzers our next question is Name an observability tool Dinesh Prometheus show me Prometheus Number two answer okay, so Jeremy you can steal here if you can name the number one answer on the board for name an observability tool Yeah, let's say honeycomb show me honeycomb Number one answer on the board. Okay team tinkerbell will play David your uh, we've got three more to pick from here You got three strikes to go the board is wide open name an observability tool Easy peasy go go analytics Show me google analytics No, come on All right marky name an observability tool Cystic Show me cystic No, who's getting this coming down to the wire here Jeremy you got two strikes and three answers Oh Let's say grafana show me grafana No Okay, so team dev oops. This is your chance to steal you got three answers If you can name an observability observability tool that's on the board you will steal the points Now also by the way, I think we all know that the true observability tool is austin parker, but yeah so I'm thinking I'm thinking either data dog or Just grepping my logs Probably go with grepping the logs roll logs. Yeah Yeah, that's your final answer wait. That's graf graf show me grepping the logs No So the points will go to team tinkerbell But let's see. Uh, what the remaining answers, uh, you have to give them the points There you go. Uh, what are the remaining answers on the board? Observability tools data dog Next answer is Jaeger And our final answer for name of observability.  Okay Ready. Let us see. What is our final question of the round is what is something you always have to google to remember how to do Okay brendan What is what's the top answer on the board? Um, i'm gonna go with invert an array Show me invert an array Survey says No, okay david your chance to steal Although wait, no, i'm sorry just goes to team tinkerbell. I I don't remember how to play our own game. Okay, good fair enough Okay, so we'll move to david david. What is something you always have to google to remember how to do? Uh could Show me how to code Programming language syntax number one answer on the board. Okay, marky. There's still a few to go Uh, you want to make sure you guys run the you all run the board or else team dev oops can steal and take it all away What is something you always have to google to remember how to do? Log into a server how to show me logging into servers Survey says No, I suck. Okay, jeremy. What do we got here something you always have to google to remember how to do? I was gonna go with uh Tower on tower syntax. That's probably programming language syntax if bash is a programming language um Always have to google Recipes let's go with cooking rest cooking food show me cooking food cooking temperatures and techniques excellent. All right back to you david Something you always have to google to remember how to do I was gonna make a painful joke, but I think that's gone with cooking there Let's go with regex show me regular expression Pulled it off there. Okay, marky chance to redeem yourself here. There's still two strikes. So you're not out of it yet not out of it yet Something that I always have to google to remember how to do Uh, take a driving test Show me driving a car No Man a pressure here jeremy back with two strikes again Yeah How to drive a car or if that includes driving directions because googling driving directions is a popular one There's no ruling there Jump start a car. No, that'll be that'd be uh, that'd be that'd be car things too. That would be that would be covered Um, how to exit vim show me how to exit vim Survey says no All right, so team devoops you have a chance to steal here If you can come up with uh an answer on the board, you will take those points Maybe spelling. I was gonna say spelling Or something related to git that or is that included of programming? That might be well gets on the parking lot I like spelling though or language So you're going with with spelling Or spelling discussing. I don't know. Yeah, let's discuss The other one I've seen but like train times bus or cinema times or something. That's kind of directions. No included Indirect at least I don't know google tells me both The weather The weather that's something you weather Do you really But you don't remember how to do the weather Oh, did you Yeah, how to spell thing I think spelling I mean I always have to google to Now remember remember that uh, this was asked of engineers clean. No. Oh, yeah, um engineers And tech related folks. Okay. Let's let's think about that. Um, I mean the one I always have to google is SSL certs Just anything to do with SSL Do you have that already? No, do we I think we didn't know Nobody mentioned that before no uh But I feel like it's more generic than that Or more random. It should be more random because the others are not too random like the others I don't know. I'm thinking spelling. I'm still waiting on spelling just because personal experience I always tell people I got into computers because I can't spell Come on make this even a little more complicated Not only if you want to be able to win everything Not only do you have to get it But you have to get a high answer because you need extra points because right now 21 points Will not send you over but if it's a if it's an answer that is a higher scoring So not to not to put any additional pressure on this Also, it all does come down to this. No, we have to get it before they get it. But then This is the final Final opportunity To see who moves on to the semi-finals What do you google? I'm at a disadvantage here Anna What do we go for? We are gonna need an answer team devoops Dinesh is leaving Okay, bye I've actually done the math and it doesn't really matter. So I say we go with spelling Spelling okay Show me spelling or grammar And it is no so the points will go to team tinkerbell And before we round this out. Let's see what answers were remaining on the board Get has to be the unguessed answers were anything with And next answer arguments or flags for the tar command And the final answer on the board was Simlinks simulink Which I was surprised that one didn't rank higher because that is definitely me Okay, so T points will go to team tinkerbell Which gives us a result Of paul checking to see if his bacon is smoked yet. I guess I think it's probably what just happened there Did he get a page from bacon duty? I think he did The point is done. Didn't we do the points? No, no, you have to award the points. They're at 96 It went up. Oh, these are the points for the unanswered ones. Oh, got it. Got it. Got it. Okay Well, that does bring us to our final results for this round of cnc f faceoff Which gives us a team tinkerbell are the champions team div oops, you gave them a good run for their virtual money Don't forget to Click on the follow button follow us here on twitch There are great shows every day of the week here on cloud native tv Oh the brack. Oh, we forgot two big things. Oh my god. Okay. Well, first of all Yes, this is as we go through the championships We will see that we had team dev oops versus tinkerbell They will be facing off against the rogues gallery in those semifinals and then we do have two more regular rounds going Next their next episode will be two weeks from today on august 11th one of the teams we're still Constructing the lineup, but it is uh sli slone and the sre street band We'll be going against another team if you would like to submit a team We still have a slaughter two available if you go to cncf faceoff dot tv slash read me You'll find out all the ways that you can participate That team name is not gonna fit. No, it's so it's certainly not it's certainly not gonna fit So we will have to re uh refactor that And again paul if we could show everyone what uh these championships are leaving ourselves up to is the cncf faceoff cup So just imagine yourself hoisting this above you at kubkan Everyone thank you for playing. We'll be sending you home with a home copy of our game and a lifetime supply of rice roni the san francisco treat the uh coming up on cloud native dot tv this week Tomorrow is a search magic with syeem, which is the fourth episode of search magic and continuing a journey Going through all of things for getting certified if you um You know the other way is by winning by playing on cncf faceoff. You're giving a cncf certification of your choice So but if you don't want to play and win that that way syeem will help you through the certification and then on Friday, we've got spotlight live with drpc With april kyle nasi of google talking about that project Registration for kubkan and cloud native con north america is now open for both in-person and virtual You can go ahead and i'll put a link in the chat for all the registration choices. We hope to see you there I will be there Every hopefully and also cloud native tv is on the cncf store So you can get stickers from your favorite cncf tv shows and i just bopped a link in the chat There as well both teams thank you so much for being a part of this and Bringing bringing your best selves or you know in the case of marty yourselves You know, uh, but either way. I don't know. I don't know why i'm talking trash your team one, so uh Maddie by the way pop said he was going to give a special prize or treat to everyone He sees at the uh kubkan that has a cncf faceoff sticker Oh excellent. Yes. So we have to be wearing it um And the more of them you're wearing the more gifts that pop will give you So you know get yourself some stickers there We will be doing some special cncf faceoff events at kubkan so We hope to see both virtually and in person and With that, uh, as always. I am your host maddie stratton. My producer is paul zarkowski We've been joined by the great team of tinkerbell of david markey and jeremy and team dev oops of brendan ice and dinesh and we will see you in two weeks One day maddie will say my name correctly and that's how i know i've made big time. I didn't say it right What did I say? I said zarkowski. Isn't that right? How do you say? They can't see your little tear


Hi, my name is Marty Webb. I'm the Chief Community Impact Officer here at TechSoup, and I'm also a CEO of Caravan Studios, which is the division of TechSoup that's responsible for working with communities and building out a variety of technology and digital solutions. Our mission at this organization as a whole is to build a bridge that helps leverage technology so that organizations working part on the ground to enact social change can benefit from a wide variety of digital solutions to achieve their goals. Today's webinar is actually a great example of the kind of connections that we're working to build. At TechSoup, we've been very engaged with data and public data in a large variety of ways over time. We worked on a multi-year, grant-funded project in Brazil that was making libraries be the center place and connection to public data. In the United States, we use government-published data and a little app that I love dearly called Range, which shows school-age youth the best place that they can get a free meal in summer time when their students are closed. And our now retired NetSquared program, one of the things abroad, Eli to us actually is on this call as well, and you'll see him pop up in the community. The first thing that we did in that program was ask people how they were using public data, working to create what we were then calling, because it was like 12 years ago, mashups of data that helped describe what was going on in communities. And what we learned in all of that work is that it is necessary to effectively use public data. It's important, and it's incredibly hard. And today, what we want to spend some time talking about is exactly how it is that nonprofits have the potential of using public data to support their storytelling and their goals and the efforts that are going on right now to make it easier for organizations to be able to access and use public data and a project that we'd like to work on with all of you actually to make it even easier still. And with that, I'm going to turn it over to our first of two speakers, Hett Maldonado Reyes is the Director of Research, Development and Analytics at Tech Impact. Hett is sitting at Tech Impact's Data Innovation Lab, which is based at the University of Delaware's Fit and Tech Innovation Hub, and they engage data scientists as fellows and work on a wide variety of social good projects. Our second speaker today is going to be Arvi Guha, a Google Fellow and the founder of the Data Commons project, which is going to be a big part of the project that we're talking about. Guha has brought many innovations to the web, including the first version of RSS, which is a service that at its core allows us to subscribe to updates from websites and is one of the founders of schema.org. Hett, I'm going to turn it over to you to get started and talk a little bit about why public data and this kind of data is useful for nonprofit organizations. Good morning and hi, everyone. First of all, thank you so much. I'm really excited to be part of this conversation and really hear with you all our thinking. Let me just start with a little bit about myself. My name is Hec Maldonado-Riz, like Marni mentioned. I'm based here in Wilmington, Delaware, but we really have a reach across the U.S. in difference. I'm the director of research development and analytics at our Data Innovation Lab that tech impact really oversees everything in technology and data for social good. So we have a different collection in the terms of data and technology and we like putting together and realizing what we can do as solutions. So for today's talk, we do a lot of our work with nonprofit and government and so the perception and framework that I have for the talk today really is about that space. So just to get us situated on conversation, when I think about nonprofit spaces, I can think about different types of organizations, but we many of us work with communities, right? So I see these pillars in green, you see them above. I see them as internal and in orange, I see them as external, but in the center and core to the work we do are programs, right? The activities, the outputs, the outcomes, all the impact we do. It's really centered around our programs. However, there are different reasons as to why at every single one of these, let's call them pillars would want to leverage data or technology for social good. So focusing on data, I'll walk through some of the examples a bit later on, but thinking about data, I just want you to ask we're going forward and think and include the reality that organization across different levels of the organization and different domains is able to utilize data and create a culture around data that benefits the organization at different capacities and we'll walk through some of those next. So to start, let me just speak to some of the more like concepts of mode of why we do analytics and how we utilize public data. Really importantly, I think that there are different frameworks that individuals can think about, but for me, these four cover really deeply the reasons and actions as to why we would analyze public data, right? First being to diagnose, you can think about that, whether that's diagnosing an issue within the organization, thinking about like operational is something taking too long? Are we not meeting the need? We really don't know what it is, right? And the second being to describe is really to go deeper and think about that diagnosis, for example, or a problem or an issue or a success for that matter. Oftentimes we get to prediction and this is where it starts becoming a little bit more technologically in data advance in the sense that predictions can be either naive or they can be models or it can be a lot of complications that go into them and our communities are complex.  If not impossible, it's just requires more leveraging. And then prescribing really is to suggest, right? A lot of this, I see it as an ordinal pathway in terms of data maturity. As we mature, we move into more and more of the right side of this range, but in general, there's still aspects of it that we do across a whole lifespan of an organization in the program. Those are the modes as to the reasons, right? And I share those first, because I always like asking individuals to think about why are we doing this, right? And I think that really importantly, having those four names diagnosed, predict, describe and report, those are really describing those are really important. However, we have a motive to the words that we do from an organizational standpoint, and it's both about the reason we're doing our words, thinking about what are our theories, what are our practices, right? But also how are we going at the opposite end and finding that translation of our word. So I really summarized it in the sense of operating, reporting and targeting. Those are the three main motives that we do analytics, particularly examples of operating are when we data collection, right? Or process enhancement, much of that is to enhance a process flow or make sure that we're being efficient or meeting and reporting then really thinks about evaluating outcome programs, tracking project progress, right? Even from a different standpoint, not from the administration of the product and maybe the length of the project patients on clients for that matter. And lastly, targeting, when we really think about it, we do a lot of different work, but it's important to also stand and look at our work at certain periods that make sense for our model and think about are we using our resources effectively? Do we still need to submit our resources to where they are? And really thinking about your whole organization's data infrastructure, right? All the different departments whether that's development and all the individual programs, there might be multiple pieces that could help you answer that question in the best fashion. So to give you an example of what these look like more and intendable, I'm going to give you a quick brief example for each one of these of some of the work that we've done on a personal end and then just hoping that it sparks some ideas that really is about the philosophy and really the culture on data behind it, that there are reasons we do these operationally, right? So this example itself is an operation example and is an example that really the goal here is we have a new email and we want to automate storing that information somewhere, right? This could be specifically on SharePoint. The examples are very valid. We are able to create a process and off the first cuck, we might not think that there is analytics behind this, but really thinking about it, you can then start adding layers of what something like this could look like for every single individual in a program and then you can really start thinking about the load of distribution of those emails that that person is receiving. If those emails translate into more engagement, that maybe might be something that that person is also receiving and does that say that your program has to expand in support or is it just a function of the process that it is today? So there's different stages and there's a lot of information that can come from what not is and I always tell individuals that for me personally I like separating the concepts of data and information because information can be almost everywhere, not just in numbers and so that's really the perception here on operations but I think that really importantly adding and adding that value is seen a little bit differently when we're doing reporting, right? So we do have a lot of reasons of data to be able to move into operations but for reporting here's an example of a social impact report for a community-based organization that we work with who is a community revitalization organization. They have pillars of success but in the back end there's a bunch of different analytics that are happening just in this front and have different domains of concept. Now for this specifically what you're looking at right now is a executive summary and we're in a presentation of access to this documentation in the back end. They're able to highlight data and just really create a story for what their work is and be able to determine the priorities that they want to set from that standpoint and the small difference here is that initially operation was an internal thing to your organization but reporting could be different for different organizations as to the operations. The last that I'll touch upon is for example in the sense here we have a map of distributions. This is some work that we did with a group here in our state and really what you're looking at is a distribution of maps right. I think that importantly is that this isn't simply just outcomes or outputs it's really thought through in terms of the geography and also just thinking about what the data that was needed and what are the different layers of data. So if you were to able to jump into this documentation there are different layers of movements of data and layers that you can add to understand or explore some ideas you might have. So with that all being said I just to summarize really there are different reasons why we do analytics there are different reasons we can take advantage of analytics for and there are different buckets or containers let's say to promote us to want to leverage public access data and civic data. So with all that being said feel free to reach out to me if you have any questions but excited to be on the rest of the call. That's great and just a couple of quick questions for you and then we'll transition to Guha and again folks please feel free to drop comments into the chat questions into the Q&A when you're describing those the four pillars the sort of maturity of an organization's ability to manage data certainly what we find for TechSoup that our answer as to which pillar we're in is all of work and how do organizations think about how they prioritize and move themselves along knowing that they're always getting in new data and having new needs and it's not just like this relentless forward progress. Yeah no thanks for that question Marni that's a great point I mentioned it as a being sequential but there is this intrinsic kind of like we're moving along so that's valid I think that for me specifically there's three major domains and I would think it's about really your data your technology and your goals right so understanding where you are in the maturity of your data do you know where your processes are depending on whether you have a tech team or not there's different stages of where you are for engagement so I think that really understanding where you are in that process from your data and technology standpoint would really align as to whether or not you're ready to prescribe right maybe you need a different space in the cloud to be able to store all that data to do predictions maybe we're not there yet but can we still prepare for that while also doing some of the other work I think that's where that there's point of like strategy would come into play from a more like goal standpoint overall not sure that answers your question for that that does that's great thank you and then I think the other thing and just as we said segue to who I was going to be sharing more about data common specifically data commons is enormous repository of public data you were talking about data the organization owns can you talk just a little bit about like your last example there with targeting in the math it seems like having ones data organized in a dependable way makes it easier to reflect against public data and see where there may be discrepancies for your population may be different than the population as a whole or it can support you in some prediction is that the main way organizations being able to reflect against these public data repositories or there are other things that's a that's a central way and I think that's a really valid point right it allows you to connect that but I think that on a separate standpoint it also allows for not just targeting but also reprioritizing there's both the targeting and demand in a way right who are your clients and I think that's where public data really comes into play there's vast information at HUD open like the census you can really understand who are your target populations and how they move over the years and over the geographies so I think that really taking that standpoint yes there is a composition but then also you can fold that into your internal organizations data right if you have a program that's for you maybe you don't want to target as if they have zero zero people under the age of 18 maybe that's the way of making that you're utilizing it is a value of knowing where to go or where to market or where to reach that's really the different stages right thank you and I'm sure that we'll have more questions as we go I think with that context about thinking about our own organizational data how we've organized it so we can start to better work with participate in public data I'll switch over to who will talk now about data comments or specifically making that public data more just accessible so to set the context as Marni and others pointed out there's a lot of data out there this data is essential for everything but using this data as an extra big thing it involves data ranking that is expensive and it's repeated over and over again in fact there's an analogy with this and satellite imagery in the late 90s NASA had this Landsat imagery up on its website if you wanted to in theory you could figure it out which of us would like from this slide in fact it was so difficult but it could be repeated and all the Google maps but really did at least in its first iterations was it did the data ranking once and for all and definitely should probably change the way we look at the world around us we have a similar situation today with data imagine you want to find out which California counties are most at full-dress food planning change the data to answer this exists out there but it's so difficult to track it down put it all together and so on very few people can effectively do it imagine a world you can just ask for this in natural language in English and you can start getting the answers that's the vision we've been working towards for the last five years our approach is to do all this data around me once and for all so that the closer the marginalized we've taken a very large number of different data sources we've done the cleaning we've done the built open schemas on top of schema.org aligned references to entities and so on built this giant knowledge graph this thing is too big for most people to download so we provide APIs on top of which many different applications can build everything we do is open source there's cloud based infrastructure for storing servings these knowledge graphs as a suite of visualization tools most of the work has been on the data itself the demographics, economics, health food, crime, education there's a lot of data around climate change and sustainability, climate, energy water, agriculture and so on to give you an idea of the scale of this piece it's about three and a half billion plus time series it's about five times the size of Fred. Fred is the St. Claes Federal Reserve's economic database that the U.S. Federal Reserve and other entities use for their decision making. We already have a bunch of applications that you could play around with this you could go to Google search today and do something like unemployment benefits in California or unemployment rate in California and this data comes from data columns or more populated things like the number of poor Hispanic women in Santa Clara County there's a ton of these queries that you can ask and get answers for all this data that you see around the statistical data that you see on Google search usually speech mostly comes from data columns let me actually focus on one particular thing today which is on helping community service organizations. There's sort of two facets of this there's a physical world where the real work happens in a food distribution building shelters and so forth and there's the other side there's the information world understanding who is most at what kind of risk using ways and even things to carbon account the context is that there are many inequities and you'd like to find out what are these inequities so that they can be there all of this data every other chance that I'm going to show you today it's data columns and they can be you can explore them today on data columns for example this is a county that will distribution of the fraction of the population with disabilities and we can see that of people in Puerto Rico people that get to clean people in counties in New Mexico where over 30% of the population is disabled there are places this is people with disabilities these are again you can see there are alarming rates of diabetes in this country or people without health insurance and in this day and age it's really sad that there are people in Ohio and South Dakota where 40% of the population is without health insurance and then on top of all this we have new factors such as climate change climate change is especially interesting because it's a great example of the data is public but sure go ahead download 70,000 net CDF files from ITCC and frame process them with news we've done all that work we've mapped it to counties, states and so on and now we can find climate change we often talk about climate change is 1.5 degrees versus 2 degrees and with this we can understand that it's not that simple this is a map visualization of temperatures in June of 2050 to the temperatures in 2006 according to one particular very popular national model and you can see there are places in this country and this is based on what is called RCB 2.6 which corresponds to roughly 1.8 degree temperature change even in this situational situation there are places in Kansas that are going to be warmer with 7 degrees centigrade in other places in Montana which are going to get colder by 4 degrees centigrade in this difference and when those kind of large differences occur we know that these different kinds of inequities are going to be worse and we have to identify who is most at what kind of risk so that we can go help them and to identify that we need data not just about climate but data about health, food, water farming, employment and so much more unfortunately this data is across the grand difference silos just at the US level when you start getting into state and county data it's an absolute challenge in order to be able to deal with it and somebody needs to go organize this and make it easily accessible which as you might guess is global submission and that's what we do but because this is so important we've decided to do this in an open fraction and make all the data open not just having the data open we're making the entire software start working not just done we're making the entire process by which we build this database it takes place on GitHub and so anybody can help participate in this they've already covered a large number of different topics from climate and water and health that all of these and many more and of course they'll be covering them to greater detail across the world but it's best to show you a simple example of the kind of thing that we're trying to do this is one of the visualization tools the x-axis is the same temperature difference that I spoke about which is according to one model in 2050 the y-axis is the prevalence of corollary heart disease across counties and then the important thing to notice is I'm pulling together data from widely different agencies and widely different things and so we're all in a single database so coming back to the chart each dot here is a US county and what's interesting here is not the correlation or lack there at all but the counties to the token block that are counties there in South Dakota and other counties in New Mexico and so on which have a high prevalence of corollary heart and may exceeding certainly high temperature rise and corollary heart disease becomes much worse as temperature increases and we have to prepare in order to help them and in order to do that we need data over a whole bunch of different things these counties I'll pick one of those counties as a umbrella county they have a very high level rate of uninsure they have incredibly high levels 20% levels of poverty even the housing is not as good as many of the comparable counties across it and you got to get the bigger picture it's interestingly it's a very young population they have a very low level of forced population and relatively low levels of college education the point is that if somebody some policy maker some community service organization is trying to figure out how to help them how did they get hold of all of this data and on the data common side we have these things called place pages for every single place so this is a umbrella county and you can look at everything from the media then come how it's changed unemployment, the poverty levels the health conditions the various health behaviors equity income education demographics and so much more I encourage you to enter the place wherever you are calling in from and see what we have to say about that every single place in the US has this level of detail most places across the world have something on the other about that the one last point is that the model is not the single data one thing to buy them all but the model is much more like the as heck and I'm already pointed out many different organizations have data that they cannot necessarily share with the outside world and so like the web you have many different websites you use the same web browser and the same model applies that way you can set up your own data columns and when you do, when you set up your own data columns the interesting thing is you can use all the visualization tools I showed you with your data columns not only that all of the data that is in our data columns is available to your data columns without actually calling or in the best way to show this is actually an example for close friends that feeding America and many of you are unsure America has put up a data column to start feeding America or which you could go visit the feeding America has this meal gap index it's their private data understandably they don't want to just take it and add it with the rest of the data columns and so their data column says only this meal care data but the way the data columns architecture is set up that data can now easily be combined with the rest of the data main data columns so we can actually ask the question that I raised right at the beginning California counties that are most at Fortress the x-axis here is a food insecurity index feeding America the y-axis is a measure of the temperature expected temperature of rice each dot here is a California county you get an idea of the counties that are most at Fortress ironically it's the actual cultural counties and it's not just that one particular measure you could go in here and look at other things like I was talking about cardiac health cardiac health and food insecurity across the US that's the correlation over here and if you say you're not interested in cardiac health you want to look into something else you have all of these variables over here that you can pay around with let's assume you want to look at something like the people without health insurance and food insecurity the whole point is that it shouldn't be this fast and here we want to go for a tap-a-tap that's the correlation it should be that fast across all of these things and you'll bring your private data in here and you should be able to pay it all we recognize that it's still all of these interfaces have been by engineers in the piece so that we can better understand the data needs of community service organizations and village tools that are much more easily usable for the range of things that you're applying I can go on talking about this we've been working on this for five years but I'll take this as far as then if there's questions we'll take them there are a few questions so let me just go through some of them here and then get through as many and then I have a couple I'd like to throw with the two of you so one from Tom Brown I've downloaded the data comments python library nationally is the data set primarily targeted to United States data? no the data has a great question the data act is not we have a lot of data about the US we have a lot of data about India we have less data about the rest of the world but the plan is to have greater depth the US just happens to have an insane trend of 11 of data because of the college department many other places in the world don't have that much data to the extent possibly and in fact you can help us build it out you can see all the processes on GitHub and you can submit the errors and add them on great and then another question coming in from just one important point one of the charts everything that you see on data comments site you can embed those charts any way you want you can get you can export the data behind those charts the whole thing is meant to be just take it and put it better you want to do what you want very specific question from PJ around incarceration rates by zip code yeah we have those side data but we don't have those let me show I'm just going to show it to you we are crime when incarceration rates are released it doesn't have population I don't know whether we have it by zip code we have it by county and I can look into whether we have it you can send an email to data comment support and somebody will answer you great thank you and then there's another question about often this kind of a question comes up often you could go to something called the statistical behavior explorer you could go here and then incarceration rates releases durations so here's just this go to the point earlier the question in chat about what's a triple this data isn't just at the incarceration rate level but it's also broken down into more layers do we have some of this state level? if you have in a lot of these things if you have a source and you're a good jealous we could import it that's great that's great the other question that's in here is one of the other questions in here is just one commenting how great the website is and wondering how often the data is updated and I'm assuming this varies right a lot of it depends on the source but I think that is an important question sources some how it's the data updated to maintain some data gets updated every week like the Bureau of Neighborhood Statistics data some of it gets updated every year and some other sensors is updated only every five years because that's something which is to the extent possible as soon as the data is publicly updated we update there's some older data sets that were added earlier than the history of the project where the auto update mechanism isn't set up but for most of the newer ones it's an auto update mechanism where as soon as the data is updated within a few hours or two days it gets it great should we have another question in here which is how does data commons different from Tableau? Tableau is a fantastic suite of tools so visualizing the data itself and there are data libraries over there but it's not trying to take all of the topic data and give you a single view that allow you to present it's one single database the focus is the happen to have some visualizing tools Tableau has a fantastic amount of visualizing tools we are trying to figure out how to put them out together so data commons is not primarily a visualization tool it's primarily a way to access that data query it, export it and then you can use it in combination with data visualization tools exactly exactly there's in fact data commons in data science forces all over the place all of it is used in Python notebooks and things like that that's great one of our team if you can drop in the data commons email address to get help that would be awesome that was a question one here and I'm wondering Guha if you could just walk us through because there are a few different questions about how you find the data with regard to geography I wonder if you could just walk us through that one more time where you start and look at that a very simple thing to do would be to go to what are the data commons are or I go into one of these tools I can pick the place it's nowhere which is a good one and you grew up in San Diego right? I did San Diego, California enter that and you get a whole bunch of stuff and then these things have entry points into other data like to explore more if you want to look at how something in San Diego has changed about grow median household it could maybe make household data by races change in San Diego over the years and click on explore more you can take it to a dozen tools you can see what happens over here or you can stay with the San Diego to stay over here you can drill down further into particular zip codes you can go up further into the county level and there's also a map to which that's a lot of people like so for example if you go to the map explorer and you say I want to look at the distribution of some variable across US county or US states or US up listed counties and you want to pick any variable over here you want to look at something like going to big health conditions you want to look at us click on that and that gives you an idea of what's the distribution across this also as you enter these these variables updates so that you can show me the variables that you does that give you an idea? yeah I just want to say before launching into some more of these questions that the project that we have the opportunity to engage on it in collaboration with our colleagues at Data Innovation Lab at Tech Impact and with Wuhan team at Data Cummins is to say how do we make this massive dataset with all of these trillions and triples and everything else available and accessible to organizations so a lot of the questions we're seeing is how do I drill down on the data how do I export the data so I can use it if I'm using the data in something how do I site the data in two different counties one of the things that we want to do is surface all of those questions so that we can provide regular sustainable answers to that and also like the instance that Pasha that the team did with Feeding America are there opportunities for us to go from all of those demographics that you see there on the side of the screen we just see the things under health but each one of those pluses means there's more demographics we can look at underneath that is there a way that we can say okay for the problem of climate change we think these are the most relevant ones let's make a Data Cummins instance that's focused on that and then provide training so organizations working in that area have a way to be able to download and export that data manipulate and use it potentially even combine it with their own data in relatively simple the most tools like spreadsheets I think that a lot of this is about how do we make this enormous resource approachable and usable for organizations so this webinar isn't the only opportunity to ask these questions and get them answered that's actually the whole point of them to figure out what you need so that we can support Data Cummins we Tech Impact Context can support Data Cummins in making this available and usable for organizations of all sizes and that it can really go in and play with it and tell meaningful stories using this public data that they can explore and target services that they can demonstrate to funders why it's different in their county than in other counties or why it's different from the experience of a particular group of people is different from the experience of another group of people and making this as available and useful as possible I said well keep going this is exactly right which is that those that I showed you are sort of general tools often just too difficult to use but we do have a toolkit with which we build things like this is an equity dashboard then it's in any geo it picks out a small number of variables and gives them to you in a dash and the hope that we have in working with TechSoon is that we will identify a small number or areas, hunger homelessness and so on for which we could pick out the right variables that make more sense than present them so that anybody can use them in a reasonable fashion and our goal is can we do something where we make these public data dashboards for organizations so we say not to limit them but to provide a starting place and actually demonstrate how to do some of those comparisons or queries and also support organizations and being able to download that data and use it in their organizations and slide decks that they might share with their boards or with their funders or to be include some of that as they do outreach and work in their own decision making so again I think a lot of the questions you all are asking are exactly what we're looking at answering with this project and the kind of information that you are chiming in on about the desire to be able to break down the data by zip codes for example a lot of people are using that and zip code is nice so I don't know if it's zip code you lived in Marnin but I don't think I no longer remember so like this is zip code 91911 that just looks nice and so yep you get it all there just zip code there that's great and so we'll be like this is a lot of what the follow-up of this project is going to be make a couple of other questions I want to pull out of here details about the queries protected so when they put in searches or they download data or other things like that I'm assuming the protection there and tell them feel free to drop more detail in the chat if I misrepresent your question but it's about the privacy of the person doing the queries there is no login right on the site we just get the data it's only when you need API access that you have a key there because we need to know what kind of queries are happening but otherwise there is no login of the data on the site and then Tom Shipman your question about tools and learning resources for people organizations who are not as tech savvy that's exactly what this project is about building actually 100% it's starting to get engaged with the conversation the 200 people that have been on this call at various points in time saying what's the data that you need and what are the skills that you need to be able to manipulate that data and useful and what are the tools that can make you ideally need to use those skills maybe less because the tool gets you closer to the end state that you need it to be in so all of that is very much what we're focusing on this project on so there's a couple of these questions there's a question from my last scene do we not afraid the data to make one data separate data but we don't modify the data sometimes we have to aggregate it we do a lot of normalizing both the actual format one of the one is a CSV one of the top limited one is on that CDF and so on so we clean up the data if we don't modify it at all but every single data point that you get the entire problem is changed so you can that's great were there others in there you wanted to pick off? just look through them there's a lot of like people suggesting data sources we have to get them and then if you are more enthusiastic and you like us like to help we're still adding that data to data comments or sitting up here on data comments please hey Marnie I just wanted to jump in on a point of it's a really valuable to go downward from an exploratory standpoint right and this tool itself shows you the ability the questions that are coming up about zip code and granularity is really important but I think it could also help from a standpoint somebody asked a question about how do I use this for an organization just to identify something from the beginning right I think that it's also useful for that strategic vision of how do we even start sometimes a place might not have resources and a foundation might be the place that's trying to make that decision right or any other aspects so it applies to different types of nonprofits but I think really importantly it's a top down approach and a down up approach as well is really what's possible from here and to the auxiliary is the integration to your data but I think that really internally it allows for that big picture view so I just wanted to highlight those too like that it is possible both directions and a mixture in between as well absolutely and I think the other thing that we often talk about with the organizations that we work with is that quite often the data that the data on a particular community or particular need whether it's a geography whether it's a group of people whether it's particular issue area actually we have most of the data we nonprofit organizations have most of the data because the data that we're using is not well captured by governments or businesses in public data that's exactly the places we are often stepping in and helping one of the things that we will also want to surface as a part of this project is where what are other data sources Guba was talking about that earlier so that they can be ingested but are there opportunities for us as a community to aggregate the data that we fold so that it can be explored in comparable ways not personally identifiable ways but in these kind of numerical and terrain series ways so that we can contribute our understanding of the lived experiences of the individuals that we work with as part of these decision making and bigger tools because so often we are the ones collecting the data one of the projects that we worked on a while ago was an air quality project in Columbia and they put up these small coffee cup sized air quality sensors in public places and that project held most of the air quality data in some of the communities because there were other public resources collecting it and so I think this is also an opportunity for us to say where can we as a collective community contribute to these general conversations I want to just give a chance both heck and Guba for you to round out and say what you hope the possibilities for working on this and what you have just shared some of mine what is some of the possibilities of working on this project are and then I'll close out by making sure functional where they can read more about this project and how can they can get more engaged just they'd like to. So heck maybe starting with you what are your hopes and dreams. Sure thanks Marty I think that I'll answer that two fold the first perhaps is that I really hope this project creates a space for organizations that are doing the field work and the community based work to be able to pick up some of their times to dedicate more of that resource into that right and data will help maybe figure out what are those resources issues that you could prioritize from a second standpoint I would love to see a collaborative kind of built from this to your point right we have the primary source of data so how do we start measuring what is a typical model that is in this particular space and how are we being successful and can we utilize that data to understand are both this big picture data that comes from everywhere and how can we connect that those two ways supporting organizations do their work and then creating a collective that allows for people to come together in that space. Great thank you Guha. Another the collaboration with the texting but one of the origins was I was having lunch at this good friend of mine is amazing given being called Emily and Emily was and I was waiting on another book all this data we have and all these tools we have and she was telling me about the studio the father-in-law who runs a food pantry in a small town in west Texas and she's like he spent his time between taking the food and making it available and driving around and writing grant proposals he does not have time on the ability or the skill levels to deal with your interface or marvel at its wonders and I realized that in order to actually have impact the data needs to be made available in an easy to use partnership by the people who need it and then what introduced to you guys and because you know the needs of the people who need the data right and so my goal is that questions like I wonder what this thing is for oh my god I got to go all this data from these different places for doing a presentation to my funders after the next grant proposals all of these things you spend less have to spend less time if we could enable that to be one of our accomplishments thank you yeah thank you all and I'll just end by telling you quickly where you can find some more information and the goals and then there are going to be a couple requests we'll offer one one more demo of how you go through and find the data and then where the extract is so that you can download so maybe you can so let me actually show you a slightly different tool which is you want to download the data you've got to this data download tool and then you could pick a particular place like in this case let me go back just the United States and then you can say I want to pick things by county or in a zip code which is what you want to right and then this is all the data that is available so let's we want to pick something as simple as the median age and median income and preview this takes a couple seconds because it's actually putting in a ton of data from different places and then once it's done with that you get a download button at the bottom which you download there you go that's your data that you download and you get a scene as soon this is one of our tools you know other tools over here I'd start off with the data commons whole page and just spend some time exploring these rooms to get stuck send an email to data commons-support data commons-support at data commons.org and then the link should be here somewhere and then let us know how we can help. Yeah absolutely thank you and to the point that Sam made in here this project is very much about figuring out how we support non-profit organizations and being able to use this data to do their own intelligence and sense making about their communities combine it with their own data where that's appropriate to suggest data sources where none exists and what where we would like to go with this in the first part is creating some communities of practice and allow us to come together around some specific issue areas so that we can support putting together some demonstration projects and say okay this is how a group of organizations did this around issues of incarceration or this is how a group of organizations use this around security so we can set up some of these instances that Duval was referring to and provide training. Eli has shared now a couple times the blog post where we talk about the project this is really introducing data commons and the use of public data and this project in that blog post there's a simple way for you to add yourself to the list and we'll keep you involved and engaged as we keep going through it. Of course we will have others of these kinds of webinars as we develop more and have more tools to be able to share with you we'll look at developing trainings and other research for your use of it over time but in general please don't hesitate to reach out and engage with us in the participation of this and we have just a couple more minutes so I'll just look and see if there are any trailing questions in here. There's in the blog post there's a list service that you can subscribe to and we'll keep you all more posted up to date as we go through it and we'll also be sharing it on the TechSoup blog and social media panels and other webinars like this one so that won't be the only mechanism we're finding out about this project in the meantime I hope you'll all leave run right away at datacommons.org and start to play with it a little bit and start thinking about how you might use it because that is the support that we want to provide over time. I know Andrew has just shared a survey it's always wonderful for us to get feedback because my colleagues and I on one of our early conversation did feedback is wonderful feels awesome but bad feedback is actionable so feel free to give us all your feedback to tell us how we can make both a webinar better but also make this kind of information more accessible and usable to you so thank you all very much for Thank you all for attending. Thank you. Yes. Thanks all. Thank you. Goodbye. Thanks very much.


This operation to collaborate with many multi-agency, not only national police, we are involved with immigration, special branch, Interpol, and the other government, like Asinopole, and so many agencies get involved to this. For this reason, I think this is very important, not only for NCB officers, but the other officers to know what is the obstacle during the process, and we can improve our process. Because after Asin is included in one community, it is very important that criminals can come to this country and help the next country with our notice. So to use the same system, this is very important to ask, to know about how to go on with and how to deal with the situation and the threats. Interpol respect of their own legitimation and their legal, their regular. So these very essential elements for this country, because it depends on ourselves, we cannot fight with the criminals from all over the world. But collaboration, everything together, sharing and joining together, we can fight with criminals and to make this world safer, we can prevent not only my respective country, but also this region from crime.


And the topic is the passive scalar spectrum at very high Schmitt's number, especially the spectral form of at the, in the very far diffusive range. This is a joint work with my colleague, the Saito. So the, let's begin with the two non-dimensional parameters. As we know in this program, the one important non-dimensional parameter is a Reynolds number. And in addition to that, there is a one important parameter for passive scalar, Schmitt's number or Prandtl's number. The physical meaning that this is a ratio of the molecular viscosity to molecular diffusivity. This Schmitt's number, in my talk, I mostly differ the Schmitt's number. The Schmitt's number can vary quite a wide range. For the mercury, it's about 0.01, and for the salinity, it's about 700, or for the engine oil, it's quite rush. This is the example of the salinity distribution in the ocean. So you can see that the salinity in the Atlantic Ocean is quite high when compared to the Pacific. Another example of the Hirsch-Schmitt's scalar is the aerosols. And the last five years, I have been involved in a study of droplets or the growth of droplets which are converted by turbulence. So in the case of cumulus clouds in the Maritime tropical area, so the aerosol is a kind of very tiny salt blown up from the sea surface. And this aerosol come to some high altitude, about 500 meters, and this aerosol can be condensation nuclei for the water vapor to condensate on it. Without this aerosol, so the nucleation of very tiny cloud droplets is almost very difficult. So with the existence of aerosol, we can get tiny cloud droplets. After that, by condensation process, these tiny cloud droplets evolve into the size of about 30 micrometers. During this process, so the heat is released, then the upward draft is generated. And then beyond the 30 microns in the radius, so the droplets begin to collide, to form the rain drops. So anyhow, if we get at least the distribution of aerosol as a continuum, we may estimate the diffusivity of these aerosols. The typical size of the aerosol is less than about 0.01 micrometers. So by using the Einstein formula, we have this estimate for the Schmidt number, about 10,000. It's quite large. So this is the example of snapshot of the smoke aerosol from mountain fire in the Far East. So this fire, the smoke aerosol can be the nucleation of cloud condensation. Now we consider the spectra of the scalar or the aerosol at the Schmidt number. In this program, as we know, depending on the Schmidt number, there are a few scaling range for a possible scalar spectrum. And for the inertial convective range, so we know, both the kinetic energy and the scalar spectra over is k to the minus 5th. And the Schmidt number is much higher than 1 than one inertial, one scaling range exists called viscous convective range. In this range, the velocity field is already died out, very smooth everywhere. However, the scalar field can be rough because molecular diffusivity doesn't work at this scale. But after some successive squeezing or stretching, the scalar field can be confined in a very thin layer about some wave number kb, which is determined by the laser. Anyhow, the one important thing about the viscous convective range is that the time scale of velocity field is independent of wave number, which is given by just a column of time scale. Therefore, the virtual wave number is determined by balancing the viscous diffusive time scale to the column of time scale. So we have this estimate. That means, virtual wave number at which the molecular diffusivity works is proportional to the square root of Schmidt number. Now let's consider the situation of the aerosol scale in the context of Schmidt number. So at large scales, we have large turbulent eddies. After succession of cascades, the fluids are discred, become small blobs. The typical column scale in the context of the cloud is about 500 micrometers, 0.5 millimeters. This is a column scale. And since the Schmidt number is estimated 10 to the fourth, therefore, the angular scale is about 5 micrometers. In between them, raindrops plays a game. The typical size of raindrop is about 20 micrometers to 30 micrometers. For the raindrops, it's about 100 or 500 micrometers. And smaller than the virtual scale, there is some scale region in which the cloud condensation of cloud plays a game. The typical size of aerosol is about 0.1 micrometers. So there is a scale separation from here to here and here to here. So this is the scale range I'm talking about. Among them, this is the inertial convective range, but mostly I'm talking about this range, the divisive range, when the viscous convective range exists for when the Schmidt number is very high. So now I explained some representative statistical theory for passive scale in this range. The key player is that rate of strain tensor. So as I told you that, we are talking about scale much smaller than column scale. Therefore, velocity field is so smooth, therefore, we may assume that velocity gradient almost uniform over some range of scale. And then we consider the rate of strain tensor. And we take some local coordinates in which that eigenvector is parallel to the local coordinates. The eigenvector is the order in this way. The gamma is most negative, the alpha is most positive, the largest one. I say I talk the three theories. One is virtual theory. In his theory, the gamma is the most negative eigenvalue of the rate of tensor. The gamma is assumed to be constant on no fluctuation, but it has infinite time correlation. In the opposite case, in the Krugman theory, it's a famous theory for passive scale, we assume that the rate of strain tensor obeys Gaussian statistics and zero time correlation. In between them, there are Lagrangian spectral theory. This is a spectral theory based on some mathematical procedure. So it assumes that the nearly Gaussian statistics for rate of strain tensor and it has finite correlation time. Now I explain a little bit in more detail. And the bachelor theory. So in his theory, in the local coordinate, each axis is the parallel to the eigenvector. So we suppose that the velocity field is squeezing. So there is no pressure term of scalar. Therefore, scalar is easily squeezed into the small size, but at some scales, diffusivity becomes work. Therefore, the bachelor assumes that they are the balance between squeezing term and diffusive term. So he obtained this equation for the scalar spectrum. This equation is easily integrated to half this one. The point is that k to the minus one and the scalar Gaussian decay in the diffusive range. Then bachelor thought that the gamma fluctuates and therefore this gamma is replaced by some effective rate of strain rate, which is given by this one. The result is that there are some non-dimensional constants, Cb, the k to the minus one followed by Gaussian decay in the diffusive range. Now, correctional model. He assumed that S is delta correlated in time. So he obtained the equation for E theta as this. The point is that this term, second order derivatives with respect to wave number, this is the expression or this explains the fluctuation effect of straining field. And one important thing is that lambda. Lambda is a triple relaxation time, which describes the rate of transfer of the scalar from through the in a viscose convective range. Anyhow, lambda in theory, lambda is constant. lambda is Cb, k to the minus one and exponential decay. Now, spectral theory of scalar. The Lagrangian theory, like the LGDI by Krachnan or Lagrangian approximation by Kanida. This Lagrangian spectral theory contains no ad hoc parameters and the set of equation is free, obtained by free systematic way. And the equation is closed by using the Lagrangian representatives, but let's skip the detail. The result is, the result, the equation for scalar is very much similar to that of Krachnan. The difference is here. The lambda is now the function of wave number. The result is Cb and k to the minus one. These are same, but in the far divisive range, spectral decay is Gaussian way, quite rapid. This is the summary. The bachelor theory, it decays Gaussian way. Lagrangian theory predict Gaussian decay. On the other hand, Krachnan theory predict exponential decay. There is some difference in far divisive range. Now, let's look at the DNS data. This is the data by the p-case group, Diego, Sydney, and Young. So for the case of Schmid number 10, the theory is not long enough, but it is apparent that the DNS curve decays quite slowly than the bachelor. Or bachelor decays, bachelor spectral decays quite fast. And Krachnan spectra is not the same, but close to the DNS. Now, this is the measurement data. So quantum number is 7. This is the data, and this is the bachelor spectra. And the Krachnan spectra, the curve is like this one, very close to each other. So now, I come to the point to explain my motivational problem. Why is the Krachnan model so good, in respect of apparent this big difference in velocity field? As I told you, the velocity field in the Krachnan model assumed Gaussian, delta correlate in time, which is totally different to the case of actual Navier-Stokes turbulence. It's a big difference. This apparent discrepancy needs to be explained. And our current status tells us that we lack some physics to understand, explain the scholar spectra in far-diffusive range. So my position is that maybe the scholar spectra in far-diffusive range is not so important, except very peculiar cases. So, however, this apparent discrepancy between observation and the theory suggests we do not know something important for the scholar spectra dynamics of scholar spectra. What functional form is the scholar spectra in the far-diffused range at the High-Schmidt number, it's my question. This is the different statement about the three theories for the time scale and the statistics of straining field. So the virtual theory sits here for the infinite time scale. The Krachnan model sits here, the zero-time correlation, but nearly Gaussian. And the Lagrangian spectral closure tells is sitting around here. But our target is here because we are considering actual turbulence effect. Okay. To approach this problem, we did some first step, we did some computation, DNS. We integrated Navier-Stokes, the scholar equation, both are excited by random force or random injection, which has delta correlated in time and applied only low-wave number range. So the range of the parameter of the Schmidt number is 200,000. And the resolution is around here. However, because we consider the High-Schmidt number, so we need it to keep the Reynolds number quite small or moderate, like the 42. This is an example of snapshot ice contour of the scholar. So you can see that the scholar feels highly, how do you say, squeezed in small scales. However, this is a botanistic field. You can see very smooth, thick, okay? So you can see that there is clear scale separation in this problem. This is the scholar spectra and the scholar spectra. And this is normalized the scholar transfer flux, as you can see. Over the one decade of wave number, the curve stays almost one. Now this is a compensated 3D scholar spectrum by multiplying k1. So the curve stays almost horizontal line. The value is about 0.57. Now we put the curatina spectra on this curve, like this nicely collapse. Why? This is the same plot, but in the semi-organismic way, the 3D scholar spectrum. So the curatina spectra slightly decays faster than the VNS, but this is a 3D plot. But if we compute the 1D scholar spectra, the 3 curves nicely follow the straight line. And similarly to this, curatina curves like this one slightly decays faster than the VNS, but close to each other. Now the problem is that a spectral theory contains, consider the finiteness of correlation of the strain field, like this one, by introducing a divisive time scale. The only remaining factor which is not considered is the non-gaussianity of the strain field. So we consider. So remember, the bachelor theory introduced the spectrum is expressed in terms of gamma. It's the most negative eigenvalue of the rate of strength tensor. It is quite natural to compute the distribution of gamma, like this one. I'll run the 42. You can see the left tail is quite long. Now let's take an average of bachelor spectra over the distribution of DNS obtained gamma. The red curve is the DNS for the passive scalar in the far divisive range. And the blue curve is computed by this spectrum, this formula. So two curves are close to each other. And we put further the largest computation to the graph. Again we can see that green curves close to the blue one. So these correspond, the curves of the two curves suggest that input, intermittence of the strain motion is very important to have the non-gaussian, the exponential decay or at least slow decay of the scalar spectrum in the far divisive range. So far the story is about the result for moderate Reynolds number. Now we'd like to explore what is the asymptotic spectrum, scalar spectrum, a high Schmidt number and high Reynolds number. We cannot do the DNS for this case because Schmidt number is quite high, okay. So to proceed further we do the theoretical analysis with help of DNS data for the rate of strain tensor. For this purpose we use the DNS data, about 835. The, our spec, the turbulence has a spectra is like this one. So there exists some inertial range. Then this blue and green curve, the passive scalar spectra, but the Schmidt number is 0.72. But we do not use this data. We just use the data of velocity field. Now we computed the PDF of three eigenvalues and PDF, Yippie zero. So you can see the left tail of gamma is quite long. The tail of PDF, PDF tail of Yippie zero is quite long. Now for the theoretical analysis we need the functional form asymptotic tail of these PDFs. Now this is a one example. This is a both logarithmic plot for the PDF of gamma or alpha. So you can see that for small eigenvalues the PDF of this power law with exponent 4 is very clear now. And for the tail, beta, beta is obtained by the fitting the straight line in this a little bit long of low PDF. The beta is about 0.6, okay. Now this is a similar plot, but for Yippie zero. For low Yippie zero, PDF obeys power law with exponent 1.50. The tail is 0.278 or whatever. The point is that now with the help of this data we assume that asymptotic PDF of gamma like this one, a pre-factor with exponent alpha and stretched exponential. Now this functional form of PDF of gamma is the problem in this one. Now we compute this integral by using steep descent. The result is k to the minus 1 and some exponential decay with this factor. By using the value of the beta 0.6. Now this exponent is smaller than 1. Meaning that scalar spectra in the far divisible range decays stretched exponential way, slower than the exponential. This is a result by the actual numerical computation on this one. The red is by using the PDF obtained by DNS. So please note that the abscissa is with the power of 0.75. So the straight line of the red curve shows that indeed there are a scalar spectra decays and stretched exponential. I skip the detail. Now almost finishing. Now we do the similar business for the spectral theory. Because we are considering the scales are far below the virtual scale, which is far below the columnar scale, therefore scale separation. Then we consider the equation which is obtained by the spectral pleasure as the equation conditioned by epsilon. So by solving this equation, we have some dissipation spectra in far divisible range which is Gaussian decay. Then we take ensemble average over distribution epsilon. The result is again we have this factor. This exponent is smaller than 1.7, very close to the previous one. So this suggests that when the Reynolds number is very high in the far divisible range, the scalar spectra decays slower than exponential. And if these stories are true, then this suggests that even the scalar spectra at a second order moment is not universal, strongly, weakly dependent Reynolds number. Let me summarize. The E theta is affected by the intermittency of the strain motion and the functional form is characteristic exponential. And the power is weakly dependent on Reynolds number. And the spectral tail obtained by DNS and exponential experiment data are not long enough in the divisible range. The collage in a spectrum is happened to be closer to the DNS, my understanding. And probably Batchelac constant is also dependent weakly on Reynolds number. So I have to click. Thank you very much.