Well today's the day This is the ultimate Christmas gift. I got for myself So I'm gonna show y'all the crib show y'all how I you know was able to get it set up and Hope you guys and you know enjoy what I was able to come up with furniture wise and stuff like that man Really at the end of the day, this is all really because of you guys I would not have the spot that I have if it would for you guys supporting me End up on the main channel man You guys have been amazing on this journey for me to you know finally get my own crib. So let's check it out All right, so soon as you walk in you have the dining room Well, this is the dining room area and it goes into the kitchen and You have the living room area shout out to you Blondie man, you know Sam come here Come on, come on a shout out to you coming to spend a couple of days with your boy fun Like I guess you could say I'm cope here and then like she'll spend like a couple of days at my grandma's house and then she'll come back over here, but yeah, so I Like the space in here. This was the thing that really sold me in the fireplace I've been rocking with the fireplace and what's dope about this is You turn this light on and boom it illuminates both sides Of the fireplace and I want to give a special shout out to ravey man She you know you guys if you seen you know see my channel at any point You know I've had this picture for a while But she custom-made this picture for me to match the other one because I knew I wanted another picture that kind of match By the fireplace area and then this will be filled with more liquor over time have the liquor cabinet area Fun fact shout out to my homie the one who helped me Mount the TV will he kind of just mounted the TV instead of the sound system stuff But there's actually a little rack to hold The like your wine glasses if I had some wine glasses Definitely, I could just put the glasses right there and it's on this side as well. So I did like that I didn't even know that was a Feature over here. I really didn't know that but definitely get some liquor right there as well and guys can see of course decent size TV I think it's a I'm not even sure what size TV this is it's like maybe a 50 55 inches.  You feel me? So we be watching Netflix shows and stuff Dining table none too crazy kitchen is cool kitchen is it's cool It's nice and spacious for the most part.  Nothing too fancy about the kitchen. So You walk down here. I usually take Blondie out sometimes this way and then you can go out this way And then that's the pool area So it's a back way to get to the pool but pool is closed right now Obviously because cold and you know, it's been kind of chilly out here lately in Houston. So You like to spot Blondie rocking with it, I've been rocking with it too many.  I don't want to be out there that much unless I'm taking Blondie out. So It's cool. It's a job done got a little guess. I want to say guess Bathroom, it's just a toilet, you know, I'm saying for the guests to use so they ain't got to use my stuff Easy peasy lemon squeezy now. Let's go into my room, you know, I'm saying if my room is basic It'll you know, you know got the got the nice soft, you know covers, you know saying in case, you know End up with a special young woman, you know who wants to come in cuddle. That's all we're doing. It's cuddling I promise you nothing else. You know, this is a family channel. Got the dress of the match. I Probably will I'm thinking about maybe getting a mirror or like some type of dresser just to fill it up a little bit This is the hoodie closet the closet that y'all Probably want to know about because y'all always love the hoodies I be having so I have all my hoodies and stuff In this closet also have all my hats in this closet as well and I have all my pants So I'll be adding more hoodies to the collection as well. I got the gift cards over here, man I got this for Christmas today. So shout out to my mom for the $50 gift card And I got this for my birthday. I just haven't spent it yet.   I'm rocking with this This was another thing that kind of made me definitely want to get this spot cuz you know, I like showers more than bath So this was perfect for me and then it came with the wash-in dryer included none too crazy some standard You know just you know for my first place. I'm enjoying it. I just space wise and Here is the best room in the crib shout out to to the month for helping me hook up the lights I got the lights in the in the you know in my game room slash Recording room. This is the shoe collection I will be doing a separate video on all the shoes that I have so far both begin some more shoes coming in maybe Probably this week. I think I got some shoes coming in either this week next week whenever so I'll probably do the shoe collection vid sometimes soon probably when the shoes whenever I get the shoes that's supposed to be coming in and then Ray be hooked me up with some more paintings in here You got the y'all seen this before when I was at my grandma's got the Shinron the static shock one of my favorite Superheroes and then she just this one actually goes in the dark But she you know she came up with this one and I was like y'all this guy be in the game room So shout out to to Ray be with the Cooking me up with the artwork. I can't even talk with the what the and you know, you see the desk This is the setup right here, you know Shout out to Doug once again hook me up with the beautiful Just beautiful mic man. This is top-of-the-line quality audio equipment You know got me my little interface forward got the console. Hopefully soon. I'll be able to give me a A playstation 5 or Xbox series X got the the light set up one monitor I'll probably get a dual monitor set up soon Also, I actually have a Nintendo switch coming in so the Nintendo switch is gonna actually be for the living room I'm gonna have my Nintendo switch set up in there and that's gonna be for my casual games Like, you know got casual gameplays Well, I'm not even gonna really record that because Nintendo be tripping But I'm gonna really just have that for like when I have guests over. I just feel like playing the game I don't feel like sweating on Call of Duty or whatever. I'll just probably play the Nintendo switch So and here's the other closet. This is where all my shirts are To you. I got a lot of shirts, bro I'm gonna be honest. There's a few shirts in here. I have not worn yet so yeah, I Have shirts for days, man. I have shirts for days Plenty of Nike shirts just different designs and that's just boxes and stuff and Chords and stuff like that. So but yeah, man, that's that's pretty much it on The tour man is nothing too crazy, but I'm very thankful for the spot I love this spot This is obviously my favorite room in the house because this is the room that I you know tend to do all my creating in so I love this this room and I love this whole crib and I'm not gonna lie to you. Well, that's it, man This is my spot. This is my crib. I hope you guys enjoyed the quick tour Wasn't too crazy. I wasn't gonna make it too long Just wanted to get straight to the point to show y'all how I got it set up And you know how I've been living out here for the past. I want to say I've been here for a month now, man So, you know, I didn't want to show anything yet until I got everything set up, but yeah, man I love this spot and I'm looking forward to creating some great memories in here. You feel me? So yeah, I really appreciate y'all for all the love and support on the channel on my channel on the main channel Once again, none of this would be possible if it wasn't for you guys You guys are amazing and all the support has gotten me to this point So this is my ultimate Christmas gift to myself.  You love, you know, you love coming over here when I pick you up But I appreciate y'all so much and uh road to 40k man, and uh, I'll see y'all next time. Peace


Hey everyone, Dylan Schumacher, Citadel Defense, and we are here to talk about layering. This is my personal system that I found for layering that works pretty well across a pretty good range of temperatures and it really just comes down to three simple pieces which is which is why I like it. So I'm going to share it with you, it's again just what I do, your mileage may vary, you might have a better system, you know, let us know in the comments but we're going to show this to you. Now listen, this isn't like hats and gloves and boots and socks and like all the other like cold weather gear stuff, we're not talking about you know, Gore-Tex layers and rain protection and stuff like that. We're just talking about basic bare like layering, okay, and making sure that you don't freeze to death when the weather gets cold. I am from the north, I grew up in Minnesota and I understand cold. I've come to find that basically there's like winter in Minnesota and basically nowhere else, it's an exaggeration, have a sense of humor. Let's talk about the three kind of basic layers and why this works, right? So your first layer and the first basis of my whole system is the smart wool base layer. This is a 250, they have like 150, 250, 350. This is just the 250, it's 100 percent marina wool and it's just a base layer. It's pretty thin base layer. Huge, huge fan of this. I'll put a link in the description box below for this one. It's an affiliate link but I really like this. I like four of these. I became a huge fan of that particular piece when I was at a SNS training solutions and we were running around and it was like, I don't know, 50, 55 degrees, 60 degrees and we're patrolling and then we're running and fighting and we're patrolling, we're running and fighting and then we're stopped and so it's a very, you know, seesaw level of activity and I was perfectly comfortable the whole time and that severely won me over to that, okay? Now, so first layer, base layer, marina wool in this case, right? Now again, you choose, you do you but I really like that thing. The second piece is what we're just going to call our exterior layer, okay? So this could be your field shirt because it'd be your field shirt if you want to roll that way. I have a smock out here right now. This is a British field smock. So big fan of the smock. I like the smock because it's great for like when things get inclement weathery. It has a hood on it.  It's pretty puffy so you can stuff other layers under it. I'm a big fan of the smock. I just I think we need to do the smock more. If there's some kind of American textile factory that you know that I can talk to about improving the smock, you let me know because I got some ideas.  Again there's three but those are the two that can really get you between like I'm going to say 20 degrees you know and up okay with just that marina wool layer and an exterior layer. Again I think picture poison. I like the smock. I've run it just in a field top before. I have the Russian top suit thing. I can't think of what that is thing is called but like the I want it Gorka. I think it's called the Gorka suit. I have that. That works great. You know whatever your poison is. I've worn this particular jacket. This is a Rothko field jacket. I've won in Woodland too. This one has a little bit of insulation, a little bit of repellent. It has a hood right so again it's kind of that exterior layer and then if I run a good base layer or two as we'll get to under it I'm good right. But having some kind of exterior layer whatever your preference is a good solid base layer again like that marina wool. You can run down to 20 degrees. Remember you're going to have kit on. You're going to have a ruck on. You're going to have your chest rig, your plates, you'll have a helmet right. You're moving so you're generating a lot of heat and because of that like I said with with less gear than you think you can really cover yourself between that that 20 to 60 degree range. I was out the other day with some boys and it was like I don't know high 20s low 30s windy super windy. You know it was like I don't know 20 30 mile an hour winds. I mean it was windy and we were out walking around dreary overcast and all I had on was the marina wool layer and and that smock and I was great perfectly comfortable whole time. No problems right so you need a lot less than you think you do. The third piece is some kind of mid-weight layer. Now for me that's just the this is the equics level two waffle top okay you can pick something else whatever but some kind of mid-weight layer to double up your insulation and really again you're going to be able to ride that down until like that zero sub zero temperature range. When you have three layers like that and a good exterior layer smock you know coat like this maybe even your field top you can ride it down pretty cold a lot colder than you think you could because again you can have kit on you're moving. Now if you stop maybe you need some kind of you know big overcoat to carry that you're going to throw on overall of your kit right maybe you have some kind of Gore-Tex layer that stuff in your pack that you're going to throw on over your kit to try to trap more heat in whatever that you can do that when you're stationary and things change but I'm talking here about patrolling active work you know moderate to high levels of activity with breaks in between right and in that I think you really just need three layers and when you get beyond that you start you know turning into a marshmallow man so I try to keep it pretty minimal. Now you notice I've talked about all tops right the only time I'm going to put a second layer on my legs because by and large I'm just running normal pants down to about 20 ish degrees I'm just running normal pants when I was out the other day in this dreary you know windy day I ran just my pants I didn't have any insulating layers on when it gets colder I'll run either you know the equics two pants or I have a set of long underwear that'll run right but something like that when it gets below that 20 degree mark that's typically where I'm going to say okay I'm going to put something else on my legs just because it's it's getting pretty cold out right you got to remember your legs are huge muscles all the blood is flowing there they're carrying yourself around right so you're like your skin might get a little chilly but by and large like you're not your legs are not going to freeze right when it gets down below that 20 degrees that's really for me again your mileage may vary that's for me just more of a comfort layer at that point okay I'd hate to get in kind of you know frostbite layer on my or frostbite on my skin or anything like that and that's a comfort preventative measure as things get colder and again if I'm triple layered on top you know I have a long underwear and I have pants on man we can we can ride that down pretty cold again at that point it's going to be less about your layers and more about your extremities you know or your gloves squared away do you have good face protection and you have a hat on like are your boots solid give good socks those are going to be the real key things at that point because your fingers will get cold your toes will get cold your your nose will get cold right those will be your issues not really your layers in your core right your core is the last thing to get cold we want to keep our core warm because then it allows better blood flow and everything but when it starts to get that cold with those three layers you're going to be just fine it's just a matter of the extremities and stuff like that so I hope that has been helpful I hope that gives you some ideas on how to layer your system let me know if you got a better one I'm always open to improving how I do layers but this is what I found that works best for me to keep moving stay in the fight and stay just you know a little bit comfortable and warm so I don't phrase it that because the cold is an enemy just ask the people who fought in Stalingrad do brave deeds and endure


Hello, everyone. My name is Mariela Machado and I'm program manager with the Engineering for Global Development team here at ACME. The COVID crisis introduced a new hurdle in the journey towards the two India CGs by 2030. It also forced the global development community to consider what we were planning to do differently in this decade of action to actually ensure the delivery in the context of future crisis such as climate change. And what are the opportunities and are for the engineering community as a part of this international mobilization for the SDGs? Today, we have assembled a panel representing cross-cutting stakeholders in global development to share their experience with crisis and recommendations for how to stay the course on their extreme circumstances as the ones we're living in now. We will hear from an UN agency with boots on the ground and infrastructure firm delivering solutions for resiliency and lessons learned from regional responses in Kenya and South Korea. Our panel will be immediately followed by a spotlight on the COVID-19 response executed by the UN's Economic Commission for Africa and their unique approach for driving innovation and investment across the continent. To start off the discussion, I would like to invite our moderator, Ariel Alexovich, and I hope I said that right, Ariel, who is a Sustainable Development Officer at UNDESA and at the forefront of the monitoring, evaluation, and strategic efforts for the 2030 agenda. Ariel, thank you so much for joining us. Welcome and over to you. Wonderful. Thanks, Mariela, and welcome everybody. Thank you for joining us for this panel, Ecosystems for Social Impact. My name is Ariel Alexovich and I'm a Sustainable Development Officer at the United Nations Department of Economic and Social Affairs UNDESA based in New York. As the think tank of the UN, our department generates, analyzes, and compiles a wide range of economic, social, and environmental data and statistics to inform and advise UN member states and other stakeholders as they take stock of trends and policy options to tackle common problems. Of course, this year our work is focused on the economic and social challenges related to COVID and how we can recover better from the pandemic. Our department also acts as the Secretariat for the Sustainable Development Goals, providing substantive support and capacity building for the 17 goals and their related thematic issues, including water, energy, climate, oceans, urbanization, transport, science, and technology. We play a key role in the evaluation of UN's system-wide implementation of the 2030 agenda and on partnership, advocacy, and outreach activities relating to the SDGs. So thank you very much to my colleague, Astra, who just gave an overview about what DESA is doing to engage scientists, innovators, and people in tech in the implementation of the SDGs. You are very welcome to follow our Science, Technology, and Innovation Forum in 2021 and also contribute to the 2030 Connect platform that Astra mentioned, which aims to bring together global entrepreneurs, innovators, students, and others seeking to exchange ideas and build networks to help SDG implementation. You can find the platform by googling 2030 Connect or I'll give you the URL here. It's tfm2030connect.un.org. So the UN does this work of engaging with the tech community because achieving the SDGs by 2030 is something we can only do by working together. Everyone has a role to play, federal governments, local governments, the private sector, civil society, international organizations, academia, youth, individuals, and engineers. So we have with us today four amazing panelists to share with us some innovations helping the world recover better from COVID and make progress on the SDGs. We have Siddharth Chatterjee, the UN resident coordinator for Kenya. We have Rebecca Moreno-Humenez, Innovation Officer and Data Scientist at UNHCR, the UN Refugee Agency. We have Myung-Hwa Lee, Research Fellow and Head of the Office of National R&D at the Research, Science, and Technology Policy Institute in South Korea. And Michael McWhorter, Vice President of STANTEC, here to talk about some private sector initiatives. So as you can see, we have four smart and accomplished guests here in only 25 minutes to hear from them. We won't have much time for Q&A, but if you have a question, please type it into the chat. If nothing else, we'll share them with the panelists and get their responses so they can be incorporated into the final document for this event. So let's dive right in. All right, with our all-star lineup here representing governments, the private sector and different parts of the UN, I would love to hear from each of you about how you or your organizations are using science, technology, and innovation to aid the COVID efforts where you are. So maybe let's hear from Sidd first. What are you seeing on the ground in Africa? To me, thank you for that question. And to me, Africa in many ways epitomizes what the future market looks like by 2050. This is a continent which will have 2.3 billion people of which 850 million will be young people. So it is going to be a market of consumers and producers. But how do we get them there? So the SDGs, to me, is the roadmap of the Africa's Marshall Plan. And that can only happen when we actually see the advent of true convergences of big data technology and innovation.  Back in 2014, I used to be the head of the UN Population Fund. There were six counties of the 47 counties in Kenya, which had the highest maternal mortality ratios in Kenya. But as a result of which, Kenya got held back in achieving, if you recall, the Millennium Development Goal Number 5, which was about ensuring that there was 170 deaths per 100,000 live birds or lower. But in Kenya's case, that wasn't the case. So we decided to go in there into a public-private partnership. So Merck, GlaxoSmith, Klein, Philips, SafariCom, Huawei, and Kenya Health Care Federation joined up with us, UNFPA, UNICEF, and WHO. And in a matter of two and a half years, we were able to actually impact the maternal mortality ratios in these six counties, which got us invited to the World Economic Forum in 2017 in January. And that's when we realized that, look, if we could actually come together and have such an impact in a micro setting, which is very fragile, very insecure, you know, the infrastructure was very weak. Why can't we look at universal health coverage as an opportunity to undertake in Kenya? And which then in 2017, we set up the first SDG public-private partnership platform during the UN General Assembly, led by the foreign minister of Kenya, and several companies, the UN came together and formed this platform. What we have done ever since is really expanded this platform to include health, affordable housing, agriculture, manufacturing, the aim being that we use as an underlying big data technology and innovation to leapfrog the SDGs. And more recently, we went to the Silicon Valley, this was in January, to bridge the Silicon Savannah here in Kenya with the Silicon Valley companies in the Silicon Valley. And it's clear that Kenya in many ways is actually at the forefront of many innovations, whether during the COVID you've seen a student body, which the UN recognized as the students of the year, the UN people of the year, because of creating ventilators, or the kind of, it's really the hotbed of a variety of innovation. So today our platform, what we are trying to do through that is drive this innovation, is drive this acceleration that we need. Because if you remember what the deputy secretary, Jan LaMina Mohamed often reminds us, she says, we have to flip the orthodoxy. And the way you flip the orthodoxy is a new convergence of partners. And therefore, I want to applaud ACME for their leadership and for having kind of pulled this whole event together. And quite clearly listening to Asha's message, I think that is precisely what we echo here out in the field. And that's the only way we will be able to propel that discourse and change that narrative, which actually besets Africa as mission impossible. Actually, Africa is where the new paradigm is going to happen. Over to you, Gary. Thanks, and Africa certainly is the full of opportunities right now. And I hope that everyone hears your words right now and is excited and sees the possibilities there. So let's just shift to your graphic focus and let me ask Myeonghwa, what are you seeing in South Korea? Asia, of course, has been a place where the worst effects of COVID have already been minimized to a large degree. But how are you or your organization using tech science innovation to aid the COVID efforts? Thank you, Ariel. I'm happy to share Korea's experience. In Korea, science and technology played a critical role in quite successfully responding to COVID-19. For example, as soon as the first confirmed case was reported in Korea, the government tried to develop diagnostic kits for massive scale tests. In order to do this, AI technologies have been used for identifying the genetic nature of the virus, so they contributed to developing test kits quickly. Also, Korea utilized smart city technologies to identify suspected patients in a very short time. The locations of confirmed patients are identified by using the details of their credit card transactions, surveillance camera footage, and GPS tracking of mobile phones. Now Korea has focused on developing vaccines and therapeutics to deal with COVID-19. And some corona therapeutics are in phase three of clinical trials. As we have seen in this crisis, I believe that the role of science and technology will become greater in reserving social challenges. Yes, over to you, Ariel. Great. Thanks, Myeonghwa. It's really great what we're hearing coming out of Korea. So thank you for presenting that information. So let me shift to Rebecca and ask what's going on with UNHCR and dealing with COVID in refugee camps. Thank you, Ariel, for the question. And thank you, Puck Engineer, for the opportunity to speak on this panel. And as you probably know, refugees and other displaced populations are some of the most marginalized and most vulnerable members of society. And now with COVID-19, they are particularly at risk because they often have limited or actually lack of basic things like water or hygiene items like soap or access to sanitation systems or health facilities, right? And so it goes beyond camps, right? So as 80% of our world's refugee population and nearly all the world's internally displaced people are hosted in low and middle-income countries. Majority of them, they live in urban crowded areas as well. So they're also at risk. In the sense, UNHCR efforts at a global level have been the focus to help first, particularly these ways, to providing with these life-saving items to support them, for example, access to water in some of the camps, precisely medical care, PPE, or hygiene materials, and also expanding cash assistance to help mitigate the pandemic, negative socioeconomic impact. But again, like the agency has double, redouble efforts on the protection front by improving community network communication and fact-based information. There's a lot of misinformation around COVID-19, particularly in in forcibly displaced communities, as well as becoming a strong advocate to respond to sexual and gender-based violence and child trafficking that have increased dramatically in the onset of COVID-19. But as your particular question on science, technology, and innovation on COVID-19, well, we have UNHCR Innovation Service, a partner up with UN Global Pulse, which is the UN Secretary-General Initiative for Big Data and AI, OCE, Durham University, and IBM, MIT, Watson, AI Lab, to create a computer simulation to understand the spread of COVID-19 in Kutapalong expansion site in Coqs Bazaar, Coqs in Bangladesh, so in the camp, in the Pocampocas expansion site. And this simulation uses a agent-based modeling approach, which is aiming to understand the movement of people within the camp and providing insights around this movement for the potential spread. And derived from this movement, the camp management team can make informed decisions. For example, which ones are the best health measures to be taken according to the movement, like closer of schools or closer of certain areas of shops or communal areas, or where to use more strongly the use of masks, right? I think the camp team, it will be benefiting from this simulation, which is already being tested with them, because they can make decisions after that evidence that we're giving, which is the spread of modeling the spread. And if you're interested to know more about this project, there's a blog that is also already in the UN Global Pulse website, and the research paper is coming soon for those engineering assistants. Over to you. Thank you, Rebecca. That is a really cool initiative. Data is power, as our engineering audience out here knows for sure. Thank you for sharing that with us. Let me turn to Michael. How about what Stantec is doing? What are you seeing in your work? Yeah. Hi, and thank you, as everyone else has said, for the opportunity. I think kind of reflecting on hearing what folks said there and also looking across our portfolio of things that we've been involved with, really COVID has touched everything that we do as communities. And I was going through Stantec has it on our website. We have a list of projects we've been involved with that have looked at this and the breadth of what technology and issues we've had to look at are really broad. Some data analysis things have really needed to be done to understand how transportation systems are changing with everybody's very, very different activities now with the pandemic. And good utilities also need to think forward and consider what all that will look like in the future as well after the pandemic, likely some significant changes there. A lot of what we've done has been trying to help businesses to continue to operate well. Some of the largest hit sectors have been hospitality, restaurants and things like that. And a really large part of what we've been doing in sort of the areas of landscape architecture and your architecture itself have been associated with trying to redesign the places where humans meet so that they are inherently safer with better more warmth because as it gets cold, the warmth becomes a challenge and also better ventilation externally and often naturally so that locations are just safer for people to have dinner and do things like that in a way that is respectful of the challenges that the pandemic has. And then there's been some very important work done with the ventilation inside buildings as well. HVAC has become a really important thing and redesigning of basically every type of building to try and address this. And I think that's been a common trend in pandemics over through history that that's been a real, real key thing. And then some things that maybe you wouldn't think about there's a lot of work has been done. Stantex heavily involved with some of our clients tracking using wastewater sampling to sort of get a forethought as to where the virus might be more prominent and things like that. So if you look at this, it's really touched every part of our life and every community that we work and serve. And so because of that, there's had to be such a broad application of technology and science to really address it. Thank you, Michael. Certainly in the COVID era, any efforts towards improving sanitation are well received. So thank you for sharing that. So everybody, thanks for laying a great groundwork here. And let me ask you guys, what do you need from clients, from donors, governments, technology itself to improve these efforts to make sure that we're reaching everybody? And maybe I could invite Sid to take a stab first, please. Perhaps the sustainable development goals itself issues a clarion call to the entire world of leaving no one behind. And therefore, sustainable development goals 17 becomes even more relevant in this era, or should we call this decade of action to achieve the SDGs. So in a sense, this new public-private partnership model that the UN actually generated here in Kenya has now been seen by many UN country teams as a global norm of advancing the SDG-17 agenda in order to look at real value propositions of, and this is not about aerial, about philanthropy or charity, no, it's about real programs that impact lives and are sustainable at the same time. And that is why what we need is what I call the harnessing of big data technology and innovation to look at this gigantic shift in the way we need to advance the entire growth and advancement of Africa, particularly which I'm interested in, in terms of the sustainable development goals. Over to you. Yeah, I think that really shows that we need more partnerships that can also connect everybody together to improve these efforts. Maybe I'll ask Rebecca from the refugee side in what can happen more to reach these vulnerable people. Thank you. Well, first, to keep up with the continued support and partnerships to tackle now what we call the double challenge now for displacement as a challenge and the global pandemic as well as a challenge. Now more than ever, I think we need the interdisciplinary approach to solve global problems now. I think of the project, as I mentioned, in this project we have social scientists, public health specialists, epidemiologists, computer scientists, data engineers, and education and shelter specialists just to design that simulation. And it's just a computer simulation. It's just one problem and one project, but that could actually lay the ground on a lot of more complex solutions that we can help those who are forcibly displaced in camps, in urban areas, whatever they're located. And then also we welcome those efforts, especially those who are focused on social impact, that they have this interdisciplinary approach and that they're human-centered based. Because I'm amazed sometimes how people design projects for refugees and internally displaced people without ever interviewing them or having any contact with them, right? So I think we need all those people that are working on solutions besides governments or technologies or even engineers to have that particular emphasis on that. And precisely just as a final point, I think also for donors for the technology sector in general, to use responsible data practices such as data protection, privacy by design, as well as a very strong focus on human rights and ethics to develop any technology solution. Because with this human-centered approach that I was mentioning, like trying to talk to those people that are affected, but also this approach on responsible data practices and human rights, we make sure that all these efforts are accessible to everyone and respecting their human rights and obviously dignity and humanity. Thank you. Over to you. Thanks, Rebecca. That's so important. Humans, humans first, people first in policies and when thinking about next steps and solutions. So let me ask Myeonghwa, in your opinion, how can scientists and engineers be better utilized to ensure that each country recover better after COVID-19? Of course, ensuring the social and environmental protections and putting us on a good path as we continue down this decade of action for SDGs. Thank you, Ariel. Let me share a recent initiative which was established in Korea. Last July, the government announced the Korean New Deal Initiative, which includes the Green New Deal. We set ambitious goals to move toward a natural society and a number of bold STI strategies. The projects are, for example, green industrial complexes, green remodeling, green energy, and eco-friendly mobility of the future. Scientists and engineers will play a significant role in developing renewable energy equipment, smart grays for more efficient energy management, and electric and hydrogen cars. I think Korea's current initiative will perfectly fit into the SDGs. Yes, over to you. Thank you, Myeonghwa. I think this sounds really cool and exciting that Korea is framing it this way. Its recovery kind of reminds me our UN Secretary-General has talked about something called a new social contract that is trying to integrate employment sustainable development and social protection. It's great to see a government stepping up to take that on. Let me go to Michael. Michael, how do you think engineers and scientists can be better utilized in these efforts? I think something that struck me personally and us as a company at the start of this was the speed of what had to happen. I think there's been a lot of examples of that. The great developments with vaccines is something that I think everybody in the world is excited about. Speed has been really important. Adaptability was a challenge. We were all sitting there thinking about what February was like and we had no idea what was going to happen or what was happening at that time and how our lives were going to change so quickly. I think in many situations we were able to respond as a species even very quickly to that. I was looking back on some work that we did. There were hospitals that needed to instantly change. We had design teams that worked on things like that and made those changes literally in hours for designs, things that we might have thought about for weeks and months before. That speed is really important. I think we have many technologies which support that now. I think to answer the question, we have these challenges that come to us. We have challenges right now that come to us. What can we do best with science? It's about really quickly sharing the great ideas that we have. Part of innovation is a good idea that turns up in one place. We need to get that everywhere. People who work as engineers and scientists and things like that, we have networks and using those networks and the technology that's available to us. We can share quickly ideas that are solutions. Around the world it's been proven that science and technology and a following of what they tell us is really the key to beating coronavirus. I think if we keep that up with our personal networks and make use of what's there and quickly share information, that is what we can do to best create impact. Thank you, Michael. Absolutely. Looking ahead to 2021, the hot topic will inevitably be COVID-19 vaccine rollout. Thank you for touching on that. Scientists put down a big challenge for engineers with a lot of these vaccines needing cold storage and mass distribution. Thanks for telling us that speed is what we need. We need all these engineers working towards this effort. Unfortunately, I think that's all the time we have. I would like to thank our panelists again for the insights that they've shared today. If you would like to learn more about the Sustainable Development Goals and ways for your organization to be more involved or you individually, please visit sdgs.un.org. Thank you very, very much for joining us and enjoy the rest of the conference.


Hi, rydw i chi a chyfrachau cyfeirio am y ddweud am gyhoeddiad yr ODI. Hefyd genna'n gyhoeddiad Gymru yn cael ei bwysig ymddir cael eu gwirio. Mae'r unig siwr gynnwys ydda i yn clydydd yn hynny'n gyfer gyda'r camlu Lloran Amelia yn gyhoeddiad Fyrdd Llyfrgell. Mae'r sefydlu'r HED контwysbydd ffócaith ei chwarae'r odi i'r sefydlu odu'r sefydlu. Rydw i fod yn nosion gyda Jenny Tennison cs ym ODI a Elewnor Sompol gyfu phall gyda Sfab Hampton, So, over to you. Oh, just a few housekeeping things. First thing is, if you're watching from the live stream, please use hashtag ODI Friday, so ask any questions or to talk about the event or if you're in the room. And also, if you're wanting to ask a question, please wait until the very end with the question to Mike. And then people at home or at work who are watching will be able to hear you. Thanks, over to you. Thanks Anna. Hi everyone and thanks for taking the time. My name is Laura Kaston, this is Emilia Katzbreg and we'll be talking about some of the research that we've been doing here at the ODI over the past two years, where we looked at data search and data discovery. We are both in the third years of our PhDs and we're part of an EU Horizon 2020 program that's called WD Aqua, in which we work on a question answering system that uses web data to answer questions. Um, yeah. Sorry. We're going to tell you a bit about the background of our work. What we mean by data, what we mean by search, and we'll outline why data search is complex, both from a technical but also from a user or interaction perspective. And we'll describe how we imagine the future of data search and discuss some of the research that we've been doing in this area. But first of all, to make sure that we all talk about the same thing. There are many different definitions of data, but what we mean by data here is information that's organized explicitly, so that's structured in some way. So imagine spreadsheets, web tables, maps, things like that. And the question that we've been wondering about is, why is data still so hard to find? Who's ever struggled to find data here? We definitely have as well. And we've been interviewing data professionals, so people who work with data in their day jobs and how they feel about searching and finding data on the web. This is what common answers looked like. To situate this a bit, broadly speaking, when we search for data, we try to answer a question with data or we try to find a data set to work with. So one, this is an example of a very high level question. Can we find links between Trump's political decisions and his personal business interests? So the problem with such a question is that it would need to be broken down into several sub-questions. And these would be the types of searches that people do to answer such a question. One of the problems with data searches that is often exploratory, so we don't really know what data we can expect to find, so we don't really know how to ask for it and how to search for it. And in the case of our question, we'd probably try to find a data set that lists the companies owned by the Trump family and also a list of all the executive orders done during the Trump administration and relate them to each other. In reality, we'd probably also try to find different other data sets and add them to it, depending on the actual detail that we'd want to find out. In addition to talking to data professionals, we've also done a search log analysis. We looked at the logs of four national open data portals, including the Office of National Statistics and Data of UK. And we looked at the queries. The queries are the keywords that you type into search engines in order to find what you're looking for. And we found that people search for data differently than they search for traditional websites. So we found the queries are shorter, people ask less direct questions, and they also put more temporal information in the queries. So that means they specify a year or a time period that the data talks about. I also understand why it's so hard to find data. We are going to work it through a number of challenges that people need to go against. Some of them are purely connected with the technology that is used to build data search functionalities. Those could be summed up in three points. Data is often hidden in files that are not visible for search engine. That means search engines cannot see the content of the file. And the only thing they can rely on when searching for such a content is the metadata that is provided. This result in vast majority of data not being indexed by search engines. And it doesn't really matter what kind of query you put. They would be just not found. Google and obviously other website engines are not directly indexing the content that is structured data. They are built to structure, to index web pages, and these web pages containing only mostly text. And this was the primary idea for the functionality. So when we think about structured data, we cannot index it straightforward in exactly the same way. And the last one, the data set is conceptually different. Search functionalities need to mirror the fact that we think and use data differently that other sources of information. For example, when we need to answer a question or a task that we have with data, we often need more than one data set as we showed earlier in the example. This indicates that we need to rethink how search for data should work in terms of indexing, ranking, and presenting the results to the user. But also if we look at data search from a user perspective, things don't quite work the same way. So it's currently not as easy to find data sets as it is to find websites. There are more steps required to, first of all, get to the data, but then also to get to an answer that might be within the data. It's also harder to keep track of where things are. You often need to click through a couple of pages to get to a download link. Or you need to go to dedicated data portals and sort of remember where a data set was sitting when you tried to find it again. And as Emilia already said, we often need to combine different data sets in order to answer a question. We also know that in comparison to reading, there are a number of different or additional skills required to find and understand data. So in order to access and find data, we need to be able to deal with different formats. We need to understand connected licenses. We might want to combine data sets from different domains that come packaged up differently that have different naming conventions or schematise and we sort of need to find the way to put them together. But also we need to be able to interpret and understand data in its context. Because if we look at something like this, we don't immediately understand what this means or I don't. And as much as this might be true for some text that's online as well, we think about data differently and it is not the same as reading. Because one of the characteristics of data is that it is not in the same way self descriptive as text is. And so we need tools that help us to be able to understand it in its context and that provides some information about the data that comes together with it. So what about the future? We imagine search for data to be equally easy as searching for websites, but also for images, for maps, for flights, for flight on the web. We think that data as a source of information is so unique that we can't just use the same approaches that have been used for other domains. Because so we think about tools that help people make sense of data. For instance, the tools that recommend data sets based on one that you already have, that are similar to it, or based on one that you've just found, or tools that would summarize data based on the type of query or question you put in a search engine. But also tools that allow zooming in and out of data collections that you find in such results easily so that you would get an easy overview of a data collection, but also be able to explore it in more detail to actually understand what it is that you're presented with. We believe that we need tools that provide context to data, that let you understand where data set comes from and how and why it's been created. Because one of the things that we realized when talking to data professionals and in the course of our research was that so much of understanding data comes actually from understanding how it's been put together and for what purpose. Some of you might ask the question of why this is so relevant or why an organization as DODI invests in such research. I think the answer is quite simple. If we make data easier to use and data search better, data can not only be used by a small number of data professionals or fairly technical people, but by many more. We think if access to data is easier and if we remove the barriers that we currently have between people and data, then we make data truly open. So we think if data cannot, if no one can find and understand data, it is not open. And with the growing amount of data that's been put online, data search is becoming more important and sort of becoming an essential part of a general data infrastructure. So going back to reality for a while, we will discuss some of our work that we've been doing in this direction. After identifying that existing descriptions of data that are provided on, for example, open data platforms, that here we have example of a description for one of the data sets on Data.gov UK, we realize that they are not providing themselves often not providing themselves useful. We decided that we would like to know what the features of summary that would actually be useful for a user, what such features should be, what kind of things such a summary should contain. So we have asked 80 people in writing task to summarize a data set for us. This gave us 400 summaries which we then ask people again to rank according to the quality. And this experiment allowed us to determine the key attributes of a summary that are necessary to give a context to the user of the data in the future. We formalized this as a template that could be provided to, for example, publisher of the data and the template consists of questions. Here we just see a few of the questions just to give an overview. Some of them could be generated automatically. Some of them used to be provided by a human, by a publisher. We believe that such a summary would provide itself useful not only to the user of the data but also could be helpful for search and indexing functionalities. That's, as we said earlier, awakened well with the text. One of the other things we looked at was how to provide context to numbers in data sets. So numbers are, on the one hand, the most popular data type, especially in data sets on open data portals, but they're also the ones that require the most context for us to understand what they mean. So looking at numbers in data sets, we were working on an approach that analyzes the rows and the columns in a data set and tries to automatically work out what these numbers might mean. So we try to assign semantic meaning to such numbers. And, for example, if you have a column in a spreadsheet that lists the populations of all countries, we would take all the numbers in that column, look at their distribution, and then match this distribution to existing knowledge bases on the web, such as Wikidata or DbPdia. And if we find a distribution that's the same or, let's say, fairly similar, we can sort of infer that this column actually talks about the numbers of populations in countries. Approach is a bit more complex than that, but that sort of describes the idea. And so the aim is to automatically understand what numbers in data sets are talking about. Because if we can do that, then we can store this information together with the data in the metadata, and it's easier to find. So yes, both the summaries that Amelia talked about and these semantic labels to numbers in data sets, both of these information could be added to existing metadata. And we see metadata as the point of interaction between a user and the data set, but at the same time the point of interaction between a search engine and the data set. So it is, in both cases, really important for data search. And the general aim of our work was to understand, or is to understand what is the right amount and what is the right content of information that should be stored together with the data set in order for it to be searchable. And with this, we hope to build on and extend existing metadata standards. So in summary, data search is complex, both in terms of technical approaches and from interaction point of view. We hope it will mature as a research area, hopefully soon. In our work, we try to look at some of the aspects that could improve data search by analysing how people look for data, how people search for data, what summaries of data should look like, how to understand the context of numbers in data sets and resulting from all above ideas for metadata guidelines. That's all for us today. Thank you for coming and thank you for listening. OK, it's time for questions. Again, can you use idea i Fridays hashtag if you're using the live stream and can you talk into the microphone to ask a question? Great, otherwise people watching on the live stream won't be able to hear you. I've got questions to kick off with. Here's one I prepared earlier. What can large data pintals do to improve how people use them to search? I think the main thing they could do is to work together with researchers or with organisations like the ODI and work on removing the barriers that are between people. This is very much a new area where we can still try many things out and it's not set how... We don't really know how data sets should be resulted in such results, for example. Removing these barriers and making such collaborations easier would be one thing. Could you give us some idea about semantic labels for numbers? Is it more than big, small or medium? A semantic label? A semantic label in our context is when you look at the data sets, you have a number of columns and each column can be described by a concept that is well known in semantic web. For example, we know that this population in a specific column is exactly the same population that is in different data sets and also that is in Wikipedia, which is described in the Wikipedia or WikiData database or knowledge base. So we would like to... Or like the ideal world, we would see everything connected to the same concepts that everybody understands and can search for. So for example, we have a column, a population of a column with countries of European Union, for example, or... Yeah, so we, for example, know that these are exactly the same populations. Maybe numbers are different, right, because information can differ between sources, but we know that this is what we mean by the specific number in a position to just having a number. So actually, this understood. So the semantic element refers to the labelling of the set rather than some kind of indication of the actual values of the numbers, because obviously it's very difficult to get numbers to be the same. They'll always be different. Yes, but we can know what the concepts... What is the concept of this number? So we know that these numbers mean population, for example, of cities in this specific data set. Is there any way you could assign some sort of figure of merit? If you have a population list, for example, could you assign some sort of word which says how similar are these lists or how different these lists are? Which one might you not trust or something like that? So I think it's very much dependent if you trust the source, right? So if you trust the publisher of the data, for example, in terms of open data ports, if you trust Wikipedia as a source, some people might say, oh, I don't trust Google Maps because Google or something similar, but I guess nowadays we all need to validate the data that we look at. So you just need to make the educated guess if the data is trustworthy. Thanks for the talk. You mentioned these summaries you were collecting from people and I was wondering how these summaries are going to be used for the data sets you're describing. How could they help? Yes, the summaries, from a research perspective, we wanted to find out how do people describe data sets? If you see a spreadsheet and you have no idea what this is about, how would you describe it to another person who can't see it at the moment? But they would be used, first of all, their meant for data publishers in order to put context to the data set and has been proven in websites we use snippets of text. So in such results you get these little elements of text that help you understand what the page is talking about before you have to click on it. And so we would like these summaries to act as guidance for data publishers on how such a summary should be written, what should it contain, what are the different elements that should be there so people can understand what the data set is about without downloading it. But what we would like to do is to create a tool that takes the elements of these summaries that could be created or extracted automatically from the data and provides them automatically and then only input the elements that are not as easy to automatically extract and sort of help people. If something like that would work, then it would be much easier to create good quality summaries for a larger scale of data sets. And so the idea behind it was is that we know or research has shown that we actually find it quite hard to create free text without any sort of narrative structure or any guideline. And so starting with such a guideline was what we were trying to do here. Is there a bloodline between searching for structured data and searching for information? So when I think about when I search for flights using Google, I don't necessarily search for a data set that contains data about when flights are happening and when they're not, but rather Google serves me up increasingly structured data about that. Yeah, is there a bit of a bloodline between structured data and information and searching for both? Yes. I mean, if you talk about flights that give that example, it is structured data, but the difference to the scenario that we are talking about is that with flights we know exactly where that data is and we also know exactly what people want to know about it. So it's much easier to build applications that work for a set scenario where we know what the data is, we know what people search for, and we can sort of create functionalities around that, whereas it's a bit more like searching within the spreadsheet even or within the database, whereas in the scenario of the open web is we don't know where the data sits. It comes in different formats and there's no schema behind that. So it's much harder to actually get to the data. That answers your question. So you're talking about this semantic part and the labelling part, but a lot of the slides, they have things like Trump company's dataset, which is obviously like a fairly ethereal search from the work if you're sitting where the dataset is. Have you thought at all about how things would work when you start graffing them? So when you have Trump and dataset and companies, then what is a Trump company? Because there are companies that Trump's heavily involved in, companies that Trump might own, companies that Trump's family might own, and they're only really like a click or a link apart and I'm sure search engine could understand that difference, but it's a question of knowing that there is that link to follow and then following it without making the person go into it. Is that something that you figure fits into the big picture? So we've been focusing on how to search for... I don't know if I understood correctly, I'm sorry. How to search for what kind of metadata could help users to input such a query as for example Trump companies, but then depending on the task you would want to assess which companies you actually want to think to account, right? I don't... Thank you. Kind of, yeah, but with the textual web you do a Google search and then you might follow a couple of clicks through. So something like related datasets, this is what you mean? Yeah, related but not necessarily related, but in a very specific predictable way that a computer would actually be able to realise like, oh and this company is owned by this company, which is owned by this company, and they have directors. Yeah, there's a lot of reasoning to do in such a case. I know that the research is looking also how to do such a reasoning over semantic web, but I think with data that is not in the semantic web that it's very much, we don't really know what is inside datasets yet. So clearly, I think we are not that far yet with for example open data or not always, maybe some of them would work, but I don't think we are there yet. I guess one of the reasons we haven't been looking in that direction and I think one of the reasons was that when we started we looked at what are actually most popular data types and it we found that on open data portals it's like roughly 2% of RDF datasets. So we tried not to focus our research in too much in the semantic web area. Going back to the semantic labels, two things. One is you expect to hope that they could be automatically generated by an algorithm. And second, do you see them, the semantic label, do you see them structured in a sort of de-weigh classification sort of thing or otherwise what sort of structure would the semantic label have to maintain consistency be useful throughout a large number of datasets? So in the ideal world, it would be automatic. Probably it's, how in a real world it would work now. It would be semi-automatic that we suggest a list of the most probable labels for given concepts. Obviously something like city and its population would be much easier to disambiguate to some specific semantic label in opposition to some concepts that are just simply are not there yet in semantic web. For example, in the Wikipedia or wiki data. And in terms of how such a semantic label would look like, it's, so in our work we looked how to disambiguate specific columns. So every column would be disambigrated to a property or a class. So for example, it would be one unique identifier from for example, the Wikipedia describing for population. So we don't know that there is this group of populations and then by the class assigned to different column in the dataset, which is for example city, we would know that this 20 datasets in a set talk about cities and populations. So maybe for query city population or London population, if we know that London is a city then we can point to those 20 datasets for example. Hi, I missed the first part for your presentation so I'm not sure if this question is really relevant, but anyway, I get the impression that you are trying to push for a standard for metadata. I was just wondering if you could give us an example of maybe an organization or maybe a local government or national government, which you think is the gold standard or one of the best standards for all of this as you're saying that semantic and metadata. The reason I ask is that I think your research has assumption of really good structured data, but the reality is also that's ideal but the reality is that a vast majority of data is very much either unstructured or uncollected. I'm just coming from my perspective, I come from the Philippines developing country. Collecting data is still not part of the priority or even in the mindset of our local officials and it would be great to see like, okay, if we, as we shift into collecting more data, it'd be great if we tried to, as much as possible, at the early stages, even structure it at this kind of level. So that's just my question. Essentially, if you've got a gold standard, a good standard for starting organizations so they can look into. If you for example think of a summary, I don't think data needs to be very, very clean if in this case, if it's maybe the automatic generation that wouldn't work as well as on a very nice destructured data, but still user, summary for a user could be done for every data set and it would already improve the user experience when searching. And maybe to add to that, if the data sets we've used for this summarization experiment, they were not necessarily clean, they had missing values as well, they had the formats of dates were mixed up and it was intentionally because what we wanted to have is realistic summaries of the actual data that is there that they didn't assume clean or extremely structured data. But I think in terms of standards, I mean there are metadata standards and I don't know, for example, the data of your case using, I think based on Decad, but I think our, or Schimeth, yeah, Schimethodog, but the things that we looked at was if we tried to think about what makes these standard, these existing fields in method at the standards, how can they be really used for search and what could we add for things to be more understandable from a user or from an interaction perspective? How can we take a step further and think of what is missing there, what could be added to that? We're not claiming to, we're not creating a standard per se, we're just looking at standards and saying what could we add that would be more useful or improve discoverability of the resources. So, for example, all of the standards do have a description field, so there is the description in those metadata standards, just when we look, nobody really is requiring to put in a specific kind of information into description, so it's everybody's writing whatever they think it's important. The most common description of a description field in metadata standards is textual description and it doesn't actually tell us a lot of what would people want to see there or what, even if we put in the things that people would want to see there, what if this would actually help set to find the data? So I thought the template you came up with was really interesting, in particular, the first question, which I think you said, that you got people to say which variables they thought were the most valuable or interesting. If you're going to summarise, if you're going to look at getting some sort of summaries, would you sort of, would you determine which ones were the most important programmatically, would you just sort of say, see what these people have said and say, oh, in general, people find this type of information interesting, so that will be the sort of thing we prioritise in the summary. I mean, yeah, that's a really interesting question, this is something we've been wondering about as well, because this was the first of a step of the sort of experience that we would like to do, but what we found is that for all of the data sets that, for all of the summaries that we had, almost everybody had like one sentence, high level, subtitle or description of what this data set actually talks about, and these would be what we would for now define as the key columns or as the subject columns, that also some ways of automatically determining the subject column of, this is the main topic of a data set. Or it could be determined by the publisher or by the person who created the data, but yeah, that's a big question we've been wondering about as well. Yeah. Any more questions before we finish up? No. Thanks everyone for coming. We've got a three week break for Easter, so we're back on the 24th of April. Can we just give Laura and Amelia one last round of applause?


So, bin is the past participle of the verb to be, and is pronounced as this, bin. Use with the present perfect, simple, or the past perfect, or the future perfect. Just like my example. He has been to Portugal. They had never been interested in pets until they got one. So, bin, expressions, bin there, done that, have already experienced.  Another expression has been, is a noun. No longer popular. They never get any votes. His has been for many people. Now, let's look at the other word, being, and this is the present participle of the verb to be, being, being. So it's a geron also, and is a noun. So used with the past and present continuous, future continuous, etc.  So they are behaving in a nasty way. So as a noun, it means creatures. For example, extraterrestrial beings are a product of fantasy. Also it means the essence. The example, the very being of these is to work and produce honey. So being can also be a geron. It's a verb form acting as a noun. There is a lesson on gerons. You may click here. Being happy is the best revenge for your enemy. Now, there are three expressions that I wanted to introduce here. For the time being, it means temporarily. For example, for the time being, I'm living in Rome, then we'll see. Another or another expression, coming to being, it means to be created. This idea came into being after our meeting. And the last expression that I wanted to introduce here is that being said, having said that. That being said, I conclude this lesson on these words. I hope you enjoyed the lesson. If you have any comments, requests, or if you want to type your own example using one of these words, you may do so under this video. Don't forget to share the lesson if you liked it. Don't forget to subscribe to my channel if you haven't, so that next week you'll get a new lesson on your inbox. So have a great day and see you next week with a new lesson. Take care, bye-bye.