Welcome, welcome everyone. We're so glad you're here. Welcome to TechSoups, leveraging public data for social impact. We're going to give folks about one more minute to join and then we'll get started. We are so happy that you're here. My name is Elizabeth Hunt and I'm a senior director at TechSoup. As people are joining, please go ahead and put your name and where you're joining from into the chat. Thanks. Okay, we've got a great group. Thank you for joining us today from wherever you are. We know you're bringing together, we are bringing together a fantastic group of people with a diverse set of knowledge and backgrounds. And we really want you to participate in the conversation and share your thoughts and feedback. So I'd like to share a little bit of the how to. You can go ahead and advance the next slide. Thank you. Okay, so first of all, please use the chat function to participate in the discussion. You can see in your toolbar, there is a chat there. And again, please enter your name and where you're joining from so that we can get a sense of who's here and build the engagement. I'm just going to say I'm in Northern California just north of the Golden Gate Bridge. We really encourage questions that there's a particular way to do that. So please use the button marked Q&A also in the toolbar. Number three, we do have closed captioning available. And you can click show captions to see a full live transcript as we move through the session. That's also in the toolbar down below. And fourth point we are recording today. We will share the recording and slides with you by email following the session. And just to note that this is the second in a series. Yesterday's session democratizing access to data will also be available for you. So I have the privilege of introducing TechSoup a nonprofit organization that for more than 35 years has sought to be an ally to anyone in the world doing good. TechSoup is the leader and founder of the TechSoup Global Network. Our mission is to build a dynamic bridge that leverages technology to enable connections and innovative solutions for a more equitable planet. So what do we do together? We move mission critical resources and solutions to local organizations wherever they are so that they can achieve their goals. There are solutions, skills, insights and strategies that help civil society improve its resilience and respond to the challenges for the future. We seek to drive impact at the organization, community and civil society sector. So our work with Google's data commons is a natural evolution of our long time focus on data as a public resource and on building digital public infrastructure. I'm going to share with you a little bit about the power and reach of the TechSoup Global Network, but I really encourage you to explore each one of the local TechSoup Global Network partners and learn about their local impact. So the TechSoup Global Network consists of more than 50 separate nonprofit organizations who share values to try to really help organizations succeed. We reach 234 countries and territories. The network is super unique. It brings the benefits of local on the ground relationships, insights, trust and feedback with a global unified mission and operational ability to deliver positive impact at worldwide scale. Since our founding, the TechSoup Global Network has served more than 1.4 million nonprofit organizations worldwide. It's collectively delivered more than 21 billion US dollars worth of in-kind technology and funding, thanks to generous corporate and foundation donors. The TechSoup Global Network operates in 39 languages and is understandable to 5.2 billion people worldwide. The simple Google search suggests that's nearly two thirds of the world's population. Now it is my great pleasure to introduce TechSoup's Chief Community Impact Officer, Marnie Webb, to share more about how Google's data commons can help you and the organizations and communities you serve. You'd think by now I'd know to unmute myself before I start talking, but no, still less than I'm learning every single time. Hi, I'm Marnie Webb. I'm coming at you from my family living room, family room in Berkeley, California, and it's wonderful to see all of you here. I see Canada and Texas and upstate New York and Illinois, Michigan, Louisiana. It's terrific. It's terrific to have all of you here. Let me share a little bit about what we're going to be talking about today. First, we're going to welcome an introduction with the panelists that generously agreed to be on here and talk with you all today about how Google's public data commons can help you use data to achieve your own mission and goals, and really how public data can inform your programs and how your programs and your data can actually also be shared and inform public policy. We're going to dive into Google's data commons and talk a little bit about the idea of open collaborative platforms as a key part of social change and cooperation across geographies and across sectors. Share a little bit of insights, actually a lot of insights, not a little bit of insights on how this plays out with food insecurity and networks that are seeking to feed others in their community, and then we'll have plenty of time for Q&A. Feel free to drop your questions into chat if that's what's easiest for you or use the Q&A button and we'll get to them as we go through the session. All right. With that, let me kick it off by describing or by introducing, I get the order wrong every time you guys I'm so sorry, by letting the panelists introduce themselves. So I'm going to start with you, Eric, and if you can just share a little bit about yourself and the awesome work you're doing down there in South Texas. I'm Eric Cooper, I'm the president and CEO of the San Antonio Food Bank, which is one of the 200 food banks across the United States that make up Feeding America. Privilege to work in the great state of Texas where we have 254 counties, and I have the privilege of serving 29 of those and so excited to share a little bit more about our work as we move on in the program but thank you for joining us today. Thank you very much. And Emily. Hey everyone, my name is Emily Ma, I am at Google, and I have for many, many years worked with across the company anyone who's interested in working on food insecurity so whether it's a technology showing up wanting to contribute in some way or, you know, an account manager who's working with a large food company who really wants to get involved. I've been sort of coordinating all those sort of broader community efforts. I'm here today because I'm thrilled to be able to talk about how we've been able to partner with Feeding America and TechSoup over the last year to drive data comments, which is an initiative that's funded and supported by Google but it's really a sort of philanthropic mission that we have. Thank you, Marnie. That's great and Mark. Good morning everyone or afternoon depending on your time zones if you're east of me. I'm in Pleasantville, Ohio. I am the vice president of digital platforms for Feeding America, but first and foremost I started in this work about 15 years ago as a volunteer in a rural Appalachian food pantry and saw this amazing need to bring data and technology to bear for the families that we serve and that we're privileged to work with across this great nation, have the unique privilege of working with our 200 food bank partners, 60,000 partner agencies and folks all across the country that help us in this mission to end hunger. Right. Thank you very much. Now I'll go into talking a little bit about Google's data comments. So, we know that data holds stories. The challenge with that is, is finding the stories. How do we dive into the data in a way that allows us to bring the context that we have from the work that we do, be able to ask questions of that data, and see answers that we can dig into. And understand more about this is a tremendously hard problem, right? There's the problem of the presentation layer. How do we share it? There's the problem of the insights. How do we query and work with the data so that we can get these insights? And there's the enormous problem of just acquiring all the data, going to all the places that it might exist in the world and pulling it together so that you can start asking questions of it. That's exactly where Google's data commons comes in. It allows anyone with an internet connection to be able to access, use, and contribute to public data. So rather than having to go to all of these different sites and get and find the data, what you can do is you can go to data commons.org, and you can ask a question of the data. You can go in and say, tell me about food insecurity in the United States. And what you get back is not a series of links that take you to different websites so that you can start exploring the answer. It's not a generative AI, where it's predicting the most likely answer to your request. What you get back is data, and that data looks like this. It comes from nonprofit contributors like Feeding America, who have made their data available in a format that allows it to be presented in this way. You get data from the global SDG database. The UN has also made their data available so that it can be shown in this way. You get data from the US census. All of that came back to me on one page when I said, tell me about food insecurity in the United States. And what you can see on this page is I could go in and download that data. And I can explore more so it provides an accessible entry point for me as I start asking questions about the data so that I can start getting insights about the data. Google's data commons is made up of three essential elements. The first one is Google's public data commons. It's what you would see if you go to data commons.org. And I was just showing that I asked that question of, and it is a place where the Google engineering team has done the work of going out to hundreds of different sources, getting their data sets, normalizing them and putting them inside this one container that allows us to ask questions and get them back in formative data driven responses. Second, it's a framework for data publishing. The framework for data publishing, and this is a place where I get super geeky, super fast, so I'm going to try and keep a lid on it for the point of this conversation. But the framework for data publishing is built on schema.org, and it allows us to describe the data in a way that is not domain specific so that it can extend and hold a lot of different data and allow you to look at data that may not always be shown together. So, Lizzie mentioned that this is the second in a series. And the first part of the series, one of our colleagues from Columbia was talking about analyzing the data and what she saw in looking at GDP and homicide data and the correlation between the two, the lower the homicide rates, the higher the GDP. And that's something that what she was saying is she doesn't usually get those two data sets together in a way that allows her to examine them together but that's exactly part of what this framework allows us to do. And then the third thing is a suite of tools so that we can actually go in and interrogate the data easily. Some of it is that AI layer I showed earlier where I was able to type in tell me about food insecurity in the United States and get back a set of answers. It's also the ability to embed some of those answers on a website, a little bit harder than embedding a Google YouTube video, but not much harder. You can also download entire data sets, or you can collect data points that you're interested in, and download those in a single CSV file, whether they're from one data set or whether they're from many data sets. And then you can use a set of APIs, which allow you to create things like the food farming and climate change website that we showed in video form early on in this. So I just want to give you a few examples of the ways that you can examine the data in in Google's data commons. These ways to go ahead and jump to the next slide. So these, there are four of these essentially one is the statistical variable explorer. The statistical variable explorer what you see on the left side of your screen is like looks like it's not that much stuff, but it's a lot of stuff if you look at the numbers next to it like economy. Like 105,635 statistical variables are under that we're not talking about data points or data sets. We're talking about the number of things you can ask a question about. So it allows you to expand that and look at it and it also gives you the ability to say actually I want to look at those variables from these sources. You can combine that in a search tool right in it. You can also use the place explorer, the place explorer like this is for the US state of Oklahoma. The place explorer just lets you put in a place and get back a set of information that has been curated. It's generally interesting about a place that compares it to other nearby places and breaks it down and each one of these things you can dig in, and you can explore more. So, next you can use the place, the map explorer, which gives you what you would expect to map. This is Oklahoma again broken down by counties and what we're looking at is the count of tornado events from 2015 to 2023, you know, shaded by county. If we were doing this live, we'd be able to click on any of those counties. We'd be able to examine more information about it and go back to other information about the economy or the demographics or whatever we wanted to look at there. And then finally, if I'm like, okay, I want to see that on a timeline. No, I can. I'm looking at the exact same data set and the same information in a different visualization. And again, I have the ability to download both this image and the data set behind the image, or I can embed it so it's live and it gets updated, you know, on my own website. So that that is just a quick tour of what's available in data commons. What we want to dive into talking about today is this idea of how tools like this, this set of visualizations, this common schema, this tool that allows us to join data helps a civil society data platform that allows us to better interrogate the data and better collaborate with one another. A huge part of that collaboration happens because the tooling around this is open source so it's, it's collaborative by design. And I'm going to turn it over to Emily now to talk a little bit about that. Thank you so so much. We're going to take a quick second here to do a sub change so if I may share my screen. Thank you so much. All right. Okay, we're ready to go right. Okay. So, I'm going to back up a step before diving into data commons and what it does I want to share a little bit conceptually why the team behind data commons society do what they're doing. So, this is me. You might not know where this is but this is a pretty incredible place I got to visit some years ago this is mere woods Cathedral Grove, and the United Nations when it was formed by Franklin Roosevelt and others. They came together under Cathedral Grove to honor Franklin Roosevelt, but to come to an agreement that working together as countries makes sense right. So, I go there pretty often to just reflect on what does it mean to cooperate. And at the end of the day, I'm both a technologist and a philanthropist in the sense that I work very close with Google org our foundation and a number of other foundations to find ways to make every future. In 2022, over $500 billion of philanthropic capital was deployed in in the country, and I bet my bottom dollar that not all those dollars are being used very effectively in the sense that there was probably a lot of duplication of work. And that kind of breaks my heart because I've seen a lot of grant proposals come through. I've been an adjudicator for a number of our open calls for the foundation on food and ag, and everybody starts with we're going to go and collect the data. We're going to build a proprietary platform we're going to analyze the data and then if we have any time left at the very end, then we're going to go do some really interesting things that only we can do because we're scientists or we're, you know, experts in the field and and that that's a little bit weird for me to sort of see all of these patterns show up and I think there's a better way. So I'm going to share a story about a fly, the fruit fly specifically. So my colleague john director was very early on in the genomics work about 25 years ago. And, you know, the genome belongs to us as people I would consider that something public domain we're all human beings and we all have genes. At this point about 20 years ago, 25 years ago, Pfizer was going to patent this right and they were going to make it proprietary and that didn't seem right. At the same time, there were academic institutions all over the world in Europe and the United States in Asia, all basically applying to the governments for grants to do exactly what I said right every academic institution was like we're going to hire three software engineers for developers, we're going to find the data we're going to build the platform we're going to engineer something proprietary and then we're going to do the really amazing science on top of that. And these are scientists right we're talking about biology departments and genomics departments they are not software engineers or developers. So the NIH and the European equivalent was like, this doesn't make sense, we're going to be doing way more money than we need to. And all of these academic institutions are literally going to be fighting for the same great software engineers and developers to build the foundation infrastructure for the work that needs to happen. So, in essence, what played out was all of these organizations eventually coming together and saying, Okay, there should be some things that let's centralize and do together and do better together. And then there's some things that only all these universities can do on their own, which is the science. Incredible professor by the name of Michael Ashburner, basically brought everyone together and decided not only on the fundamental infrastructure. So the source code to develop everything on top of, but a common language to articulate what was happening. And so rather than you say to motto I say tomato right they all agreed that it was going to be tomato. And that made the genomics research world completely explode in a really positive way. Everybody got further together faster. And that's why we come together and do the open source work so you might ask what is open source software what's, you know, a standard taxonomy. Right, there's three characteristics. It's really, really straightforward. Number one, it's open so you can go on the internet and basically see all of it right none of it is opaque none of it is transparent. Anyone can read it anyone can understand what's happening with it. And nobody can suddenly stick it in the wall garden and hold it for their own. The second thing it's it's shared. So I actually believe that in certain cases, it makes sense because the problem solving if there's errors and there's issues happens faster if there are more eyes from more perspectives on the common infrastructure. And then finally it's flexible I think this is a really, really important thing for me to double down on because a lot of people are like oh open source it must be free right and it must be only for nonprofits that is not the case. There are many, many different kinds of licensees, the most free and permissive licensees like Apache and Creative Commons for you can build a for profit company on top of open source software and the truth matter is, you're probably using it right now. You probably, if you're on a computer right now, if you are on zoom if you are have if you have a cell phone you're probably using open source software in some format right now. And there are some very, very big for profit companies, including Google, who have benefited from open source, and we also in turn give back to the community. So we have a very large team that basically gives free software and data Commons is an example of that right so you give but you get, and that's how this world has put itself together in the last 30 years. So, again, open solutions are not necessarily best for everyone I'm going to articulate why.  That's probably not for you you probably want to protect your intellectual property. But in the case where we're trying to solve for systems problems where you know the situation is distributed it's complex there's many organizations are involved. You know we probably all want to come together and share in the benefits of building a single sort of infrastructure that we all collectively choose to build together and define the requirements together. The second thing is when standardization matters. Open source can be really really positive so again. Yeah, I've mentioned you say tomato I say tomato right. It really helps so we speak all the same language right and when and that helps us share so I will share an example of schema.org and how that really helped the job market actually and recruiting firms get a lot better at this. And then finally, in many cases where there is a triple bottom line effort ongoing transparency doesn't matter right when it there's a social justice component when there's an environmental component. Transparency helps to be able to really get down to the root of what's happening. Okay, so I had the privilege to work with Marnie and Mark and a number of organizations around how might we move more food surplus food in the country to where it needs to be. And we were building out about a year ago something called open product recovery, and we chose to make these standards and the reference code public because we saw a couple of characteristics and want to make this concrete as just an example of why we decide to not be a closed walled garden but an open source project. Just to kind of illustrate why. So number one, food waste and food surplus and food recovery is a huge and distributed problem there are probably 100,000 organizations in the US working on recovering surplus food and moving it to people in need. The second thing is standardization does matter and I keep going back to tomato versus tomato. I'm using looking at the records of food that's been recovering and how it's been described and you know it's a bit of friction right so I'm trying to describe I have a ton of, you know, tomatoes and and somebody in the northeast doesn't understand what I'm saying they might not say yes to taking that lot of tomatoes and distributing it for me because they're not sure what I have. Another thing is, there are a lot of silos in the space, and it takes time to find the right organization to recover that food so how do we shorten that timeframe, especially because food doesn't last forever. Right you know there is a ticking time clock on on you know those tomatoes. And then finally how do we build the trust and the persistence together with this organization or these organizations and so we wanted the transparency in order to ensure that everybody could see what's going on and and and not feel like there was something they had to work through in terms of the opaqueness and the silos. So three more case studies and I'm going to hand over to Mark and Eric to really dive in since they really are the experts in the space so there's the parable of the old man and the sons. And the idea here is that if each of the old man sons has one stick, you can break the stick very easily. And what this old man did trying to demonstrate to his sons to work together was he bundled the sticks and it's much harder to break the bundle sticks once you have them all bundled together. So, three examples from from my world that you might already be using number one. This is my favorite because I think many people have a cell phone. And about 20 years ago again, something called the open handset alliance came together and this is why there are 3.5 billion users of Android phones in the world. So, I phones about 1.5 Android 3.5 billion users, because every single handset manufacturer out there was trying to build their own proprietary hardware proprietary software and it was costing everybody a lot of money. So, they came together and said let's come together and agree on a set of common standards about the software and the hardware, and then we're all going to win together faster. And so this was what actually drove the proliferation of mobile phones in all sorts of parts of the world that might not have afforded it in the first place. Again, you might ask, wow, Google does Android great. You must make a lot of money off of it. I will also mention that it is an open source project you can find the source code of the Android operating system. And companies like Xiaomi and China literally have built incredible public for profit companies with Google making no money at all. So, we were willing to make that bet because we believe that the pie was big enough for everyone and we were going to create more pies together versus fighting over the same piece of pie. Okay, second example that I mentioned earlier job postings. If you can believe it, many moons ago before multiple tech companies and multiple companies in the sort of job and recruiting space on the internet came together. Everybody had a different format for how they would describe a job posting, right? It's kind of mind blowing, right? You're kind of like, okay, well, there must be sort of a title, maybe a salary range, maybe like a location. Like you would think that everybody would agree on like how to describe a job posting, but no, everybody thought it was proprietary. That was kind of silly. Everybody came together and said, okay, let's agree on a common way to describe a job posting. And what happened afterwards is, you know, monster.com and career, you know, development.com started cross listing their postings, right? And sharing in the revenue, it was better for users because if I'm looking for a job as a developer, I would be able to see a job posting for a particular company on multiple sites versus having to go to one site for job postings in the northeast and other site for job postings in the southwest, right? They would all cross list and they would share and everybody benefited as a result. I believe this is my second to last final example. This one is one of my favorites because it really demonstrated Feeding America coming together. Eric Cooper is on this map. Mark Moncoff is overseeing a lot of the work with his colleague Stephanie Zidek. A couple of years ago, I had the privilege and honor with my team to work with Feeding America to see what we could do if we could put all the sourcing data from across the country of seven food banks into a single platform and see if there are ways that we could get better together, right? So what we learned from even the initial analysis just before Thanksgiving about four years ago was a lot of food banks were sourcing from the same growers. So Thanksgiving dinner was actually more costly than it needed to be, even when purchased at a discount. So if they could purchase together from the same farmer, 10 times as many cranberries versus each one pound of cranberries on their own, one ton of cranberries on their own, right? There was an opportunity there, but that required each food bank to share into something called member data sharing services. That has now become something incredible, some 50 plus food banks are part of that, and they're seeing so many opportunities to now work together as a network at that level, but it required the bravery and the courage to put their data into a common platform. So this was a public blog if you want to read it, I will share the link shortly. And then final example, I will leave you with this, as philanthropists, as policymakers, as technologists, I want to see data comments at the end of the day really help us help our government representatives really articulate their stories in a data driven way. So I had the opportunity to speak to Congressman Duarte. He represents the 13th district in California. He has some really incredible counties that are part of our food production system in the United States and beyond. Here is a really quick map that would have potentially taken him a couple of months to put together. So you can go on to data comments and literally type in show me the maximum temperature projections, based on the climate work from the IPCC, compared to food insecurity, and show me how this lays out on a scatterplot. Right, we know that for a fact that as temperatures rise as heat becomes an issue that that will aggravate all sorts of things and that will trickle down to impacting food insecurity. So should we not focus on the counties in California where I live, where there's already significant food insecurity, and the temperature is going to rise more than other counties so I'm not worried about Santa Clara County where I live right now, and the two million people as much as I am worried about Merced County, because they are not only experiencing more food insecurity now per capita, they are going to see a maximum temperature that's going to be much greater than what I'm experiencing here. So I literally click three buttons to get this map right I would normally have had to download from the IPCC a bunch of climate data figure out how to use it, download a bunch of data for feeding America figure out how to combine that with IPCC data and then maybe finally get an answer. Data Commons makes quick analyses like this very, very, very easy. So I'll stop there. Happy to discuss more. You know how to find me Emily Ma at Google.com. Happy to answer any questions. Over to you, Marnie. Awesome. Thank you so much, Emily. I've got a couple questions for you but I'm going to save them until we get to the question and answer part and prioritize questions that come in folks feel free to drop them into chat or drop them into the Q&A. As Emily said, our colleagues from Feeding American National and the San Antonio Food Bank, Mark and Eric and I think Mark, you've got some slides you're driving for both of you. Is that true? That is correct. Awesome. Yeah. I will hand it over. Yep. Hopefully you can see my slides. Okay. They're beautiful. Okay, well, I don't know if they're beautiful or not. I am an IT guy and not a Google slide expert. And in no way did I have Bard actually helped me with my slides today. I probably should have. But what I wanted to share is one of you of the Feeding American Network. I know there are, we're well known across the country, but maybe not so well known on a broader and more global audience. The Feeding American Network and we always use the big end that our network is a big tent and that hunger is everywhere. 200 plus food banks across our nation, 60,000 partner agencies. So individual charity organizations that come together as well as our national office organization that helps us work to end hunger every day in this country. Using the idea of the public data for really big good. Our network itself is, as I said, 200 food banks across the 50 states, the District of Columbia and Puerto Rico make up our network. And then we have affiliated relationships with the global food banking network as well. I mentioned big data for good big good. This is a title I used to use for a for a Ted style talk that I would give when I was working at one of our largest food banks here in the here in the country. But together we have this bold aspiration, which is to cut food insecurity in half to below at or below 5% by the year 2030, and more importantly to reduce place and race based disparities in all of the places across this country that we serve. Food banks, food pantries that are part of this network serve every zip code, every census track and hunger lives in all of those places. I'll do the same kind of thing that that Emily did maybe not as articulately as she did, but these are some actual slides. From the old school way, I'll say circa 2017-2018, when we were trying to do really deep analysis to understand hunger localized. First we started with a whiteboard drawing in it and my CEO from my food bank coming over and saying, I think we need to put a food pantry here and here and here and here on a map. And, you know, in the Edwards Deming famous quote of well that's great but let me see some data about that. We started with that premise, we stitched together all of these data sets, the public American Community Survey data. The CIS shape files are internal private data. And what it allowed us to do was take the premise of this of this great hand drawn map from my from my whiteboard to, okay, well now we can break. The maps happen to be Franklin County, Ohio or Columbus, Ohio for for folks who are football fans or not. Ohio State University is located kind of right in the center center of this area. One of the things that we always knew about our community was we understood the impacts of prior decisions over the years, all the way back to things like the redlining maps from the early 1930s to the interstate highway system that cut through Columbus. Like every major city in the country. This little weird map that looks like a T down in the lower center of the center. That's right where the highway system cut right through neighborhoods. And guess what those are the areas where we're still seeing poverty. We use data to create some highlights of like, okay, these are the hotspots of where we know there is poverty and hunger. We further went on to marry up our most private data, I'll say, and this map is a little bit hard to read and that's a bit intentional because every dot on this map is the rooftop. Within 110 meter precision of neighbors, we call them neighbors or clients the people that we serve of their households over the course of a year and a half's worth of services. This created a bit of a heat map that you can still even see the outline of that upside down T for how hunger was disparate. The disparities around hunger were centered in 15, what we called zones which made up about 150 census tracks. This took a long time. In fact, this took several years because we had to download, we had to write API's to figure out how to get data from the American Community Survey, marry that up to our own internal data warehouse, match it, geocode all of those rooftops and get to this data. This allowed us to do something quite remarkable. When we were looking for places where those dots ought to be in my food bank CEO, a gentleman by the name of Matt Hobash who's the longest serving CEO, second longest serving CEO in the Feeding American Network. In his 39th year, we drew that map and he said, oh, I think we need one here. Well, in fact, it ended up being two census tracks to the west. And a few months ago, Good Morning America came out to film a segment and it aired about hunger in Ohio and generally hunger across the United States. The place where they were filming this interview and they were in one of the food pantries, a large food pantry operation, sat right in the census tract of where the data told us to put it. Now, that was a lot of lift to get us there over the over the years. The promise of data commons and bringing our data together helps us do that just as Emily showed in her slides, with a few clicks. So one of the things that we did was share something we call our map the meal gap data. This is a large data study that Feeding America has done for years and years that helps us pinpoint where are the most hungry people in our country. And we best serve them. We took that data, which has always been available on our Feeding America website, but shared that into data commons in such a way that we could begin to map things like food insecurity data in data commons. So today, you can go to data commons.org type in what I did here which is how many people in the US face food insecurity and up comes the data. So if you were to type in substituting Texas for for the US, this would drill into that we could then ask further the questions. What counties in Texas, well, my friend Eric is going to talk about the 29 counties that are part of the San Antonio food bank in Southwest Texas and Bear County, where San Antonio is but also all of the rural areas hunger is very localized. Then one of the amazing things as we began working with data commons last year, we in partnership with Emily and Guha and the entire team from data commons created data commons dot feeding America.org. One of the first questions that I was like asking in fact when Guha and Emily and I met for the very first time was an example that we had tried to do with our Department of Health, which was to match up data from the 500 cities project for those of you familiar with the CDC data around health and health disparities. I said boy wouldn't it be interesting to see the correlation, or the intersection of hunger and heart disease for example, and in two clicks, it was up on the screen. And that was one of these many thousands and thousands in fact, a nearly 30,000 data sets connected into data commons dot feeding America.org is that was instantly accessible to us. That allowed us to have a view of this for folks who go to data commons dot feeding America.org you'll actually see this on the homepage and a link right through to this particular graph. When you hover over any one of these dots it'll bring up the exact county that's represented by that dot. And then it'll let you drill into about 80 data points about that county, a county and its surrounding areas. So just a tremendous tool that we would have never ever been able to build. I mean, maybe if I was at it for 100 years, but not in the way that we're able to do that now. So a really powerful tool. I want to turn over to Eric. Eric and that you know full disclosure Eric and I have been working together on this work for the best part of a decade since I had the pleasure to start working with him. He is one of the most dynamic leaders in our feeding America network has been honored in many ways. And when he's not on webinars like this, you can usually find him out in service to people who are facing hunger, or you can catch him at a water burger somewhere in southwest Texas. Eric, I'll let you talk a bit about your network, and we'll roll through these slides. Well, thanks, Mark. Well, I am just so excited to be a part of this panel like I called my mom this morning saying that I was going to be on a group with with Google and tech soup and feeding America. And so it is like, I'm giddy, but I'm hoping to maybe take it from the cloud to the ground right how how these tools are maybe delivered. I just want to point out what Emily shared earlier. And that is this open source concept as a as a operating nonprofit serving needy Texans. We are oftentimes supported with philanthropy that comes with a level of restriction. And so if you're ever tempted to give a gift to an organization and impose a platform that doesn't hold to these kind of strategies of cooperative data sharing and information, those closed platforms that sometimes are forced on nonprofits can sometimes create redundancy of the nonprofit managing multiple platforms. And so the workload to do that. I encourage any of those vendors in the space to play nice play well with others. I love the, you know, alone you go fast but together we go far so we use a framework of today, tomorrow and a lifetime, and it really is meeting our neighbors immediate needs today and groceries and meals. But we really bring household stabilization with our tomorrow work which is enrolling families and snap and wick Medicaid long term care for seniors children's health insurance programs, some, some federal agencies that sometimes our neighbors are unaware of or maybe they can't navigate those state applications effectively and so we really do a lot of work in that space and actually, it's the larger benefit of then the physical food that we, we deliver and then lastly our commitment to really help people work according to their ability and receive according to need we have our lifetime work which is dedicated to workforce development job training job placement and we absolutely can't do that alone we've got a great cohort of nonprofits and, and higher education that delivers some amazing tracks that move our families forward. So the 29 counties I mentioned earlier this is what it looks like it's a, it's a large section so if you, if you want to think about the drive from Coke. I'm a Diet Coke guy but this is the real deal down to LaSalle it would take you about eight hours. So it's a, it's a pretty good distance that we cover a lot of, a lot of miles, a lot of land. But in these 29 counties is roughly 2.7 million people. And so our opportunity is to use that public data that Marnie talked about I mean, boy population data, poverty data USDA has food insecurity data. And so that kind of displayed in our map the meal gap to better understand where the population and need lives. And then our opportunity is really combining than the local data that we're collecting to gain insights and so that's a big step from talking about food insecurity to nutrition security right because that's ultimately what it's about it's about making sure that our neighbors are nourished. In our food for a lifetime we, we also deliver nutrition education and our nutritionists often say to me, Eric it's not nutritious unless they eat it. And that, that challenge of the right food right amount right time is something that local food banks are trying to do but we're really trying to make sure that we, that we get the food that families need to better nourish their children. Many of them are dealing with chronic diseases. And so, as we look into our donation stream, we, we handle hundreds of thousands of SKUs so different product types and data that it can be overwhelming. We lump it generally into dry refrigerated and frozen just to talk about it but there's all of this data that that we have. Sometimes when you think about where food comes from that's donated to a food bank it's, it's food in the margin it's, it's short dated it's, it's close to an expiration date it might be really ripe produce and all of that food that comes in sometimes can be criticized like hey, did you really get your neighbor the best food. We have this data point of pounds and sometimes pounds are probably talked about more than anything, but it's just a measure, we can convert pounds into meals. 1.28 pounds is roughly a meal, and then we can talk about what it's worth you know what is the value of that but oftentimes that people are saying Eric what are you doing to nourish our neighbors, what are you doing around nutrition security. You know we, we struggle with, well we got a lot of data. And so, in partnership with Google and Stanford we really looked at our inventory mix what what we took in and, and you know, how was it comprised of that refrigerated frozen and dry. We learned from this member data sharing that Emily talked about with a cohort of food banks, all of our inventory was kind of combined. And we looked at the commonalities. We also then looked at, you know, USDA's guidelines around healthy eating, and it's really highlighted what's called the healthy eating index this is kind of an algorithm that really helps. So all of us understand the quality of the food that is nourishing our bodies and so here's some of the data you can see kind of what the healthy eating index is all about and so we are committed to try to procure as much of the best quality as the most nutritious pound of food. And in partnership with Stanford and some great minds and students they started to look at. All right, what is the makeup of the inventory that's actually in our food banks. This graph just shows man the complexity of some of those items and the nutritional breakdown. It is started to get exciting when we started to see like where do we trend, compared to the average American diet right so if someone's going to the grocery store and getting their own food, you know where do they score. And then, because food banks are distributing food it's a self selected variety because you know we don't have all of the items in the grocery store we just have what's donated. What was the nutritional mix of what was donated. You can see this starts to trend so on this graph you can start to see the average American diet, compared to the US food supply, and the average American diet scores just below a 60 that's just individual shoppers. And then, when you look at that compared to the average food bank inventory those within the member data sharing, you could see that the inventory mix is actually healthier coming from a food bank than the average American diet so as I try to explain to somebody hey, what is the nutritional mix of the food that the food banks distributing. It's data that's coming from again those national sources with those local sources that really start to paint the picture. Now I know as you're watching this you're going, what happened in 2016 and we are trying to unpack yeah exactly what that that anomaly is and, but we would, we would not have known if it wouldn't have been for for Google, Google Commons, Feed America, TechSoup, and our good friends at Stanford. So, you know, how do you start to really make decisions and you know I think these leading indicators, Marnie teed up, you know, policymakers, when you can start to show what's actually happening. feeding America as a tool called service insights on meal connect that gives us the opportunity to look at real time data so you know the national data from USDA and what the food and security rate and the population and need in a county might be, but combining it with our real time data helps to educate people on what exactly is happening. And it gives us the ability to pivot, which we had to do in San Antonio at the onset of the pandemic. Many of you might have seen an aerial photo that was taken by newspaper here in San Antonio called the Express News, and a photographer named William Luther, and he captured an image which was a line of cars that were gathered at a food distribution. And prior to that day, we were looking similar to the data that Mark showed around the T, we have a smile. And some of our local nonprofits maybe inappropriately call it the poverty smile. It's this, it's this belt that as you look at it. It's smiling back at you but it is the population of our community that really struggles our neighbors in need and and we had set up this distribution at a knowing that we needed to be right in community. On the south side of our city at we found a large parking lot at a swap meet called traders village that had a huge huge tarmac for us to be able to meet the need that we could see coming from the real time data and boy, we were prepared to meet that demand it ended up being one of the largest food distributions in America. 10,000 cars 50,000 people 22 semi truckloads of food, son up to sundown. We were able to meet the need by forecasting what we were seeing in our data analytics to make a pivot to send more food, get the ingredients that the families would need. I woke up the next morning to calls from, you know, USA today CNN Dr. Phil to talk about 72 different countries around the world wanted to understand the line, and what the pandemic was going to be ushering in in families meeting basic needs. You know, I stayed to the end of that distribution and made sure every car was served, but it was in loading the last minivan that I met a husband and wife and their three kids that when I, when I, when I met them I wanted to apologize that it taken so long and they said they knew it was going to be a long wait, but they were so grateful to get food. They shared they met working at this hotel in our downtown and got married, bought their home and started to grow their family. They said they knew something was wrong, you know, when the general manager called them to come into the hotel at the same time because they worked. One worked the day shift when worked the night shift, and, and the general manager just said hey, no guests, no, no money, no jobs and they were both laid off at the exact same time. And they, they got into that minivan and drove home worried about their health insurance, you know where they were going to, if they were to get the virus how would they handle that they were about making their house payment they were about how they're going to nurse their children. And as I loaded those groceries into the back of their minivan the three heads of their children popped up and they were just so excited to be able to, to have nourishment that we would have not been prepared to meet that demand had it not been for data to be able to act pivot and meet that need so excited to answer some questions. I truly am grateful to all of those that have made this possible. All the employees at Google, the great partners at TechSoup, I have been in this work for about 30 years. And I've seen the work at TechSoup and how they're impacting so many organizations so thank you for joining us and I'll pivot back to the group. That's terrific. Thank you, Eric, Mark, Emily, so much for the storytelling and for diving into what data can do for us. So now, you know, let's just open it up for some questions and answers so feel free to drop any questions into, into Slack or into the Q&A. I'm going to start with a couple of questions that I have and Eric, I'm going to start with you, actually. What one of the things I hear and what you are talking about is just your desire to serve the whole person. I think that's who you're serving. You're not just serving the hungry belly or the individual that can't make it, you know, afford to go to the grocery store, get their food needs met in conventional ways, but the whole person. And that requires collaboration with other organizations in your community. So I'm just going to say how you see both shared sources of public data. So you're sort of, you know, able to equally access public data and some of the leading indicators that you were talking about, but also how you see the ability to share data between the organizations, data that you all may have as helping one another, you know, either meet needs or identify future needs or whatever else may come. Yeah, Marnie, thank you for that question. I, there's so many aspects of information, as Mark mentioned, 60,000 agencies that the 200 food banks serve and in our 29 counties we have about 875 of those 60,000. So these are independent nonprofit organizations, churches, schools, and, and as they talk about the need, as they see the lines that are forming at their senior center or homeless shelter or food pantry, our ability to share the insights of the demographics of that population, and, and what's trending at the food pantries. One of the biggest variables is using this information for sustainability, right, it's data that can be used in grants and proposals to better get the support that these 875 organizations need. Another aspect is that that local proprietary data that comes through our member data sharing in the inventory and so what's the financial impact of the food that we're distributing together, right when you think about, you know, I had one of our 825 partners come to me and said, Eric, we realize that you're our largest donor. And I'm like, Oh, well, you know, when you value the food that a food bank might be providing a partner, and she said I challenged all of our other donors to match it. Wow. So, it's just been incredible to be able to take to share that information so that everyone benefits and not just using the data to solely benefit one organization. Thank you very much that that actually leads wonderfully into a question I have for you, Mark, you know, data sharing in this way at the organizational level is challenging when we share data and I'm, I'm going to include tech soup and that we share data we've often locked it inside a PDF, and we're sharing it so nested into our own insights that somebody else can't take it apart and interrogate it, or have different insights. I think what you all have done not just to making your data available on your site but so that I can query it and look at it against USDA data or SDG data. I think feeding America is informing me with the same level of provenance that these other organizations are but also I can interrogate the data, I can download it and use it in the same way that that I would think takes a lot of organizational courage, and maybe some a long time persuading and I'm wondering if you can talk about what it took to get to a place where you could share data in that kind of systemic way. Yeah, I'm not from Texas but I do wear a bit of a cowboy hat at times. So it, yes, it takes a bit, it takes a lot of organizational courage, and, and even a personal level of courage to say, we want to set this free because there's so much more to learn from it. The examples, the few slides that Eric ran through about the project that the students from Stanford did along with the Hoover fellowship was a perfect example of that. We had a data set of about 6.6 million lines of that receipt data. And honestly, Eric and I have laughed about this the only thing that we were hoping to achieve out of it by releasing this data was to just figure out how many apples versus applesauce versus apple butter and figure out the butter and apple butter aren't the same things across all of this data. And the brilliant students who we then let that data go to we, we did anonymize it in a way that could be reverse engineered so when the data came back to us we could at least get a sense for what food bank it came from. And we, when we set that free, we were hoping for outcome a well to char and the other students from Stanford who worked on this project did something totally unexpected. They said, after they figured out the apples the applesauce and the apple butter. They said, well let's smash this up against the USDA data and the healthy eating index, and out came what we just showed this idea that food banks aren't all full of tuna and tuna helper. And that is such an old narrative and an old, I talked about old school and data assembly. Now that's like old school cassette tapes when you, when you roll back our network on as a whole, we just use well more than a billion pounds of fresh produce every year, and moves that through to roughly 18 and a half million households. So, setting the data free, having the courage to do that provides a way for somebody to see something in the data that all of us who are so close to it wouldn't have seen or wouldn't have asked. It's, it's the thing that when, when we first got introduced to data commons, one of the things that Guha had as a RV Guha the founder and creator of data commons said to us was, you know, this can be a tool for citizen scientists, citizen journalists and citizen analysts. So that's why we, we wanted to be bold about this. That's awesome and and I do think it takes a tremendous amount of organizational courage and putting a stake in the ground it would be wonderful actually to you guys were up for it to document some of it in a case study because I think this is a challenge organizations face how does that impassioned integrator, convince the executives in their organization or the board in their organization to release data in a way that allows other people to manipulate it I think that's what gets so scary you know we want to own the all of the narrative but this thing that allows for people to build narratives based on context is tremendously valuable. Emily and also you talked when you were talking about the value of open source and I think that's, that's part of convincing one another that we should be releasing our data right is that it can allow for collaboration you talked about what agreeing to a set of data standards, and implicitly sharing with those data standards meant for you know genome research right. The example I often use when I talk about open source is slightly more dire example it's of the Oakland Hills fire in 1991. When the Oakland Hills were burning and San Francisco firefighter showed up to help their hoses didn't fit the hydrants. So they brought their trucks into the hills and couldn't help because they use two different standards in these neighboring cities, and in fact the firefighters that came in from around the area, couldn't, couldn't connect so I think you, you have this spot where we're talking about the explosion of innovation and possibility and new discoveries that happens. We're also talking I think about the ability to collaborate when in unplanned unexpected events. Yeah, it was the firefighters had the intention of collaborating, but they would need those fire trucks in those hills was unplanned and so they didn't have the infrastructure to collaborate. Eric, when you were talking about the line of cars during the pandemic, that's a great example of we have the intention to collaborate. We have an unexpected event, and we need to have the infrastructure to collaborate now I wonder, Emily, if you can talk just a little bit about a little bit more because you did talk about this but a little bit more about how how opening up standards and making our data help provide both the potential for a explosion of possibility, but so that we can come together when circumstances drive us to do something unexpected. Sure, sure. So you know Marnie your story your dire example is heartbreaking I'll share one that's a little bit more uplifting that 100 years maybe having to do with communication so if you can believe it. Many, many moons ago, every country had a different gauge wire for telephony. Right, so we had to have these crazy switchboards all over the world to just talk to you know a friend in another country right so I'm Canadian if I have to call home from the United States to Canada is like you know, oh my God the two countries use completely different gauge wire right and this was how it all started until you know in international group came together realize that this was silly and let's agree it's all like it's proprietary what gauge wire. You use the build your telephony systems. And so that allowed for international communication to be way easier and less full of friction and so you know, um, maybe rather than digging deeper I'll share a couple of thoughts in terms of, you know how I think about this. You know, it doesn't matter if you're in government or an academia or a nonprofit or for profit. I oftentimes ask myself the question of okay we have a challenge in front of it. Should we build it ourselves because nothing exists. Should we partner with someone to do it together, or should we just buy it because there's actually an example out there that is like ready really well developed and kind of incredible right. And, and I think oftentimes and I am a fault right it's like, well, if it wasn't built here it's not good enough right that happens actually I have some incredible engineers I've worked with her like I really smart so I'm going to build the thing. Somebody else has already built it and maintains it right so I would encourage folks out there whatever organization you're in. What is the end goal that you have and what is the most efficient way to achieve that end goal and it might not always be, you know, you yourself building it, you might end up in a better place by either collaborating to build it together, or literally buying it but already exists and I think with systems challenges, such as food insecurity, that question becomes really pertinent because we have so many people waiting outside, you know, like looking for food support, and there it's, there's not a lot of time and not a lot of resources and we have to connect that and so maybe that's my answer for you is, you know, rather than specifically a data it's philosophically how do we approach problem solving how do we step up and be the best that we can be you know in the food waste space I say a lot about you know where is where does this food serve its highest and best use and sometimes it's not necessarily going to, you know, a hunger relief organization it might actually be better to send it to animal feed and I would ask philosophically and abstractly that question of everyone here and data same same here right like do you need to keep it for proprietary right what what purpose does it serve. Are you doing it because you know you're afraid that you know that there might be adverse consequences and are those fears true. Right. You know I think I, I, I, I, oh, a world of gratitude to mark and Eric for doing what they did because they really led the way and showing what partnership and collaboration could do, but they had to overcome. They're individual fears but they're organizational fears and I would queer I would interrogate the fears that you have in sharing. And, and, you know, actually I was talking to Julie Yerco yesterday another member of the early seven, and she said, you know Colin Paul will say that optimism is what amplifies and I prefer to live my life. You know, in an optimistic fashion. How about that that's a very long winded answer, not very much about data, but I really truly believe that it comes down to querying why. Yeah, why we are default not sharing right. Yeah, I think that's a that's a great framing and I love the framing of optimism and possibility in there and also I think it's at the fact that so much of data is social. We collect it because of social interactions it happens because of social and you know but it also, you know, whether we share it or not. Our response to it is how we show up in our community. And you know and I think when we start thinking about how do we move to a mode where it's the, you know, the default is share. You know, when we've gotten to software as a service we don't have to think about saving our documents anymore, you know, in quite the same way. And, and now, you know how do we move data in that direction how do we move where the default is sharing and one of the things that we talked about when we were prepping for this section session is the role that foundations or governments can take in helping shift the default. The question though is, how do they shift the default without putting the burden on small overstretched organizations to say now suddenly you have to become an expert in data standards, and you have to become an expert in sharing, and you have to, you know, abstract your data so that it's not attached to Marnie web. You know, it's, it's a person with this demographic profile that that got it got a set of food that's being aggregated with other people so that we can say something about it and I wonder, Eric. If you can go around and ask all of you actually, but if you can start off talking a little bit about what role you think foundations have in helping create this data infrastructure, recognizing this tension. You know, the possibility of transferring the burden to the small organizations. Yeah, I'm already thank you and you know, I think, for me, it's the fear of the fruit smoothie right smoothies are delicious, they're easy to consume their nourishing nonprofits want to be a fruit salad right. They want to they want to know that they're identified as a unique fruit within a bowl or a collaborative. I think what's cool about data Commons is, man, they do you right, they make sure that the source is credited that that your identity is protected right and I think that's one of the critical things because nonprofits have to tell their story they have to show that they're delivering, and so that the foundations are giving to them. But I think, from the encouragement to this audience like, encourage the nonprofits to be in that fruit bowl. And if it starts to drive to a fruit smoothie, if you start to feel like you're all blended up and lost. I know that you contributed know that you're contributing to outcomes. It. I mentioned it earlier the redundancy in neighbor referral platforms is where we see it most often. There's a dozen different, you know, platforms to screen for social determinants and make sure a patient of a hospital can navigate that's food insecure but then if they're housing insecure and all of those things but each one of those systems are generally closed and causes the nonprofit to decide, do I take the funding, or do I work on two different platforms and I think for foundations, your role would be to give and not to disrupt and I think the disruption is being done in these data sharing and and and working off the outcomes that are seen. So what I hear you saying in there is we want to maintain the profit provenance of the data we want the individual nonprofit to be able to show up with their contribution, but maybe foundations have a role in helping set up that structure for sharing. And they can do some of it in the way they ask for reporting or other things like that that doesn't blend it in and make it be the foundations fruit smoothie. But instead, they're the bowl that the nonprofit fruit salad is in. I'm going to probably beat that metaphor to death over the next six months so thank you for that, Eric. Mark same question to you what do you see in the role of funding agencies and helping us move towards this future that's about data interoperability. Well, you know, morning, thanks. I came from an agency I started as a well as a volunteer in it in a rural Appalachian food pantry. But what I've always seen in this work is that while as Eric said, you know, oftentimes the nonprofits get boxed in by by the funders or the folks that are trying to control many of the levers. But what we've seen is by telling a better story, and in having conversations just like this and connecting the data, we can help actually educate the funders the foundations. And in others about how if we break those those things that bind us that way bind us to a particular paradigm or a particular methodology, then the agencies, the nonprofits, the community organizations can band together. One of the practical examples that I've that I've seen work in community is when you allow individual agencies, one as you say to have their own their own identity, and their own place in that in that fruit pole, but they also self select into working against each other, because they can then they're not competing against each other. We started out talking about the whole person, the whole person comes to the food pantry, the clinic, not just the hungry person not just the person with a diet related disease or challenge. When we see agencies band together released by data at a great example of food pantries that I worked with personally here in Ohio, where five food pantries who were all in the northeast corridor of our community, like all started working together dynamically, not because we as the food bank or the community said, Oh, hey, neighborhood services and St. Stevens Community House and Worthington Community Food Pantry you should work together. They were all self selected. And what they figured out by looking at what was their precious data that they would never ever share. And by releasing it to each other, they realized, wait a second, we're not serving zip code 43219. We're all right around it. None of us are serving it. Well, let's do that.  And I think those those can transcend can transcend just the charitable food system. And while that's been the focus of what we're talking about food is a part of a larger and broader issue around poverty and systemic racism that extends across all of our country. That really resonates with what our colleagues in Columbia were saying yesterday about their experience with data commons and the role of civil society organizations because they have territories in the country that are invisible from a government data perspective because they were on the other side of a civil war and there weren't government services there for 50 years. You know, and they they they instituted the political and paper part of a peace process but you know the rest of it the infrastructure part of a piece of process is still coming and so they were talking about the exact same thing that data helps you identify gaps it helps you bring local context and people into the conversation and that the ability to then step in and fill those places is a tremendous opportunity. Emily I'm going to change the question a little bit for you and and really say, you know, you know, I understand the company you work for has had some success at going to scale and serving many people around the world in a variety of ways. And, you know, I think, I think Google in making data commons open source in the investment in using and developing schema.org, you know, has, you've set the stage for scale, but it still requires investment for that to become a platform and I'm just wondering what you see is the, the investment around that platform that needs to happen you know is it is it engineers that are you know building radar graph visualizations into it is it it you know is it people setting up data commons is it foundations, owning some of that infrastructure. Thank you so much, Barney. We try. Let's let's start with this before I dive into some some concrete examples. I've worked for a number of companies and Google is not always right. We are humble enough to recognize that we stumble our way into the future. We try very hard to do the right thing at the right time, and we'll get it wrong sometimes right and so if Guha we're here to say this, he would say to all the food bankers as we did back in June when we had a workshop together, we're probably 90% wrong and how we're doing this so help us get to 89% wrong right and give us feedback along the way and we're going to make a product better. We've done this for many of our platforms like if you if you don't know this Google maps actually gets updated every two weeks, for example, right because we know that the world changes and we need to be accurate in the representation and it's dynamic and so I love working for this company because of its humility and its willingness to sort of lean in and to work across the board, whether it's a nonprofit government what not to get better dynamically over time. Let me share a concrete example of the sort of ethos of how Google works with platforms like data comment so I mentioned the open handset alliance way back when Android would not be what it is today. If it weren't for millions of developers on the Android operating system so you know anyone doesn't you don't have to be a Googler you don't have to be a staff member, you can go and build an app and put it on the marketplace and you know get users onto your app. We've done a lot of the hard work to sort of build the fundamental infrastructure along with partners and, you know, other parts of the world, so that you can go build the app, whatever your niche is whatever your expertise we want you to be able to get it out there as easy as possible. I see data commons in a similar fashion. You know, when I sort of go back to the roots of this effort about five, six years ago when Guha started this, he also founded schema.org. He wanted to really create a proliferation of sort of citizen scientists as I believe Mark had mentioned earlier, enable you know even at you know I have I have a 14 year old nephew who's starting to ask questions right how can we enable him without having him to, you know, have to have like, you know, a data science PhD along his side to sort of interrogate public information and bring it to his classroom right. So, you know, as I see the platform growing the investment that we hope to make is to really help any organization, any individual, whatever sector and start to ask questions right I encourage you all to go to data commons.org and ask it a question. Right, just like you might go to Google.com and ask it a question, ask it a question. And if it doesn't, you know, serve of what you want and tell us support at data commons.org the team behind is incredibly responsive. We will make it better over time and that that's how we've made Google search better Google mouse better over time is because we get a lot of interesting negative feedback the platforms and it gets better over time so that's that's my hope for data data commons.org I'm super excited now that it's the sort of foundational platform for the United Nations sustainable development goals. I see it really sort of exponentially becoming useful over time because of all of you, because of hopefully millions of people who are looking to use data sets that are already available but not really accessible, fully accessible in the world, better and faster. That's great. Thank you very much. I am, we're at a minute so I'm going to say thank you to Emily Mark and Eric. It's a pleasure not just to have an opportunity to ask you questions in public on this panel but it's been a pleasure to get to know you through this joint work and I'm, I'm inspired by the work you're all doing to help make sure that in the moment people have the food they need but also think about the system that needs to be changed so we can get to a better planet so thanks so much for your work thanks so much for the time today and to all of you participants. Thank you very much for being here. Please, we'll be sharing a recording of the session will show the resources that we talked to by encourage you to go to data commons.org and play with it. And do tell the engineering team, you know, their, what works and what doesn't work their, their motto is negative feedback is actionable. And so they take that that hard feedback and they turn it into the next thing so knowing what doesn't work is as valuable as knowing what does so thank you very very much, and we'll see you again soon.