Autolight and its 98,000 dealers bring you Mr. Richard Widmark in tonight's presentation of Suspense Tonight, Autolight presents the story of a man who tried to learn a lesson the hard way. He bet his life on the turn of a card It's called the card game. Our star, Mr. Richard Widmark This is Harlow Wilcox with the $100,000 question. Have you signed up in the Autolight family charity drawing? If so, you may be one of the 25 persons selected to name your favorite church, hospital, school, or other recognized local or national charity to receive a big share of $100,000. To enter is simple. Listen to this on-the-spot interview in Bronxville, New York. May I have your name, please? Yes, I'm Mrs. William Keith. I understand that you've signed up for the Autolight family charity drawing. Is that right? That is right. When you signed up, was there any trouble to it? None whatsoever. I just signed my name and address. And no obligation? No obligation. Well, that's fine, and may I wish you good luck. Thank you. So friends, sign up tomorrow at any or all of these Autolight family car dealer showrooms. The Soto Hudson Plymouth Studebaker Dodge, Willis Nash Packard Kaiser or Chrysler. Not much time left, so don't wait. And now Autolight presents transcribed Mr. Richard Widmark in the card game. Hoping once again to keep you in suspense. That's five dollars. Yeah, that's five dollars. Me with a little paradox. I'm out.  Too much. What do you say? I'm faking. That's right. Happy Euregon. And five more. Betting into my pair of A's, Euregon. Well, your funeral. I'll see you, Euregon. Euregon, have a fire. I'll call you. Triplets, Euregon. Three-eighths. Beats me, Larry. Another day, another dollar. Let's see what happens. Excuse me, gentlemen. Tough on you, betting into my three-eighths with a lousy pair of queens? It was a good pair of queens. It's not good enough. Yeah, yeah. Well, that's the way the game goes, eh? I know how the game goes, Larry. Don't look so sad for me. I'll win it back. Sure you will. Sure you will. Eh, you weren't thinking of quitting for the night, were you? Well, as a matter of fact, I was, yeah. I thought you wanted to play poker. I did, but I'm broke. That's enough. Real two-bit player, aren't you? A clock in here was sixty-seventy dollars and figures you'll take us.   Yeah, well, let me tell you something. I'm gonna get lucky some night, and I'm gonna clean up on a game. Sure, sure, sure. Now you go home and dream about it, kid.  You're not a big-time gambler.  And don't come back here for a game anymore. We don't like the way you play. I knew a man in Bayonne once.  But me, it hasn't happened yet. But someday it will. But the babies do in a couple of weeks, Jenny. You shouldn't be working. I called your office this morning.  Six jobs in six months, chick. Well, I didn't want to worry you, Jenny. I guess I'm just not cut out to be a real estate salesman, but you don't have to go to work, honey. Yes, I do. I have a very special reason. We haven't got any money. I'll pay the rent, Jenny. How? Well, I can't lose forever. Look, Jenny, if I can just get a steak, I mean a good steak. And get in the right game, I... Well, I know a luck will change. You've got to change, chick. I thought I could change you once. I even thought you were changing when you told me you'd take a real job and all. But it's no use.  You always will be. Always waiting for change of luck. There's just no cure for you. Well, honey, we've had better days, lots better days. Don't say things like that. Don't you see, no matter what you win or lose, it won't make any difference. You'll always be wanting a bigger steak, a better game. Look, I knew a man from Bay Old Monster. I know, I know, I know. $75,000 in one day. What do you suppose that $75,000 meant to him? When he had it, it was just another steak. Well, let's not argue about it, Jenny. I don't want to argue any more, chick. I know it's hopeless. It's so hopeless that I'm going to divorce you. Jenny. Don't put your arms around me, please. Honey, I just couldn't make it without you. You'll have to, chick. I mean it. But, Jenny, what do you think I... I have to leave pretty soon. When I come back, I hope you'll have your things packed and moved out of here. Look, look, suppose I got another job. Would that help? Would it cure you? Sure, sure it would. If it's a good job where I could make some money. Honestly?  That's just it. If I can get enough money to get into the right kind of a game, I can make things all right, darling. You wouldn't have to work or worry about anything. Chick, I can't do that. That's why I'm asking you to leave. Come along with me all this time. Yes, hoping all this time. But now things are different. We're going to have a baby, Chick.  We can't go on this way. You've got to get a job and keep it and stop gambling. I'll get us a house, Jen, and some money and... Well, I've got to have a change of luck. Change of luck? Oh, Chick. Jenny. Jenny. Jenny. All I need is a little luck and I can make it all in one night instead of working 40 years. Tonight I can make it before Jen leaves me. I know I can't. I can just get in the right game. I can just figure a way of getting into the right game. Always like to make clothes for you, Mr. Regan. No trouble at all with the fitting. Yeah. I have some nice wisters and some imported flannels if you'd like to order some today. Oh, I'm afraid I don't have the time today, but I'll get in as soon as I can. How much do I owe you for the suit? That's gone up a little since you were in, Mr. Regan, 300 and tax. All right. Just send me the bill. Sure. Uh, did you still drive that fancy sports car you bought in Europe? Oh, yes. Once you get used to one of those things, you don't want to drive anything else.  Wow. Well, the prices you charge for a suit, it shouldn't be too much of a joke. I have overhead, Mr. Regan. Terrible overhead. My tailors come pretty high. $200 a week for a master tailor. All right. Well, I'd like to borrow your car, won't you? Hmm? Now, how long have I been getting clothes from you? Well, off and on. Ten years, I guess. Well, then don't look at me as though we just met this afternoon. I'd like to borrow the car for tonight, in favor. Of course, Mr. Regan. Here, it's out front. Thanks, thanks a lot. You're thinking of buying a car just like it, and you want to try it out, that's it, isn't it? Well, let's let it stand that way. So, what'll you do with the back? Tomorrow morning, okay? Anytime's fine. Wait a minute, Mr. Regan. I've got to tell you what's on my mind. What's that? Well, first, I'll tell you that you'd be surprised the four flushes I've got on my books who couldn't pay $30 for a suit, let alone $300. I'm remembering that you've always paid me cash. But I haven't seen you in a year, and the day you came in, it asked me to send you a bill. I'm not worried about that. Well, you're worried about something? Like I said, it's been a year, and I've heard that things haven't gone so well with you lately, and I'm wondering if the suit and car may have something to do with it. Mr. Regan, you wouldn't do anything foolish, would you? Walshy, I'll tell you something. Now, you know that there are some people who are impressed by expensive clothes and fancy cars and all that stuff, and sometimes it's a good idea to impress them. Now, tell me, does that sound foolish? The next was to sit down and figure how do you get into a big game with no dough? That one I couldn't figure. I had an idea. I had to find out if it had worked or not. I knew one thing. If you want to win big, you have to take chances. And I took one when I walked into the bar. Mr. Regan. Hello, Harry. Well, like old times. Well, it'd be, Mr. Regan. Well, let's try your scotch. Sure. Haven't seen you around for a long time, Mr. Regan. I've been out of town, Harry. Vegas? Yeah, Vegas. Where's your suntan? I stayed indoors. I guess you did. And water, soda, what? Just like that, that's fine. It's smart. Only way to take it. Sando's doing all right here, huh? In every department. Out front and behind the front.  No, I don't think so, Harry. Just stop by for a drink. What? You know about the big game Sando has every night? Real big. Yeah, I've heard something about it, sure. Well, you tried a kid. What? You never in all your life ever came into a joint like this just for a drink.  Well, hello, Harry. Oh, good evening, Mr. Sando. Chick Regan, isn't it? That's right. The door man appointed you out. He raved about your car. Pretty nice thing to get around here. I like it, Jim. Hey, now I remember. You do? Yeah. You was pit boss for me in one of my places. You used to call me Mr. Sando. Did I? Well, let's say I've changed, Jim. Okay, let's say that. Buy a word? No, no, no, no, thanks. So fill us up again, will you, Harry? Sure. Anything wrong? Well, just wondering about you. Oh, why don't you wonder somewhere else? Real cocky these days, don't you know? That's all right. I like that. It means you got those. Say, Chick, the crowds play back in there behind those curtains. But upstairs I have something else going, something special. Here you are. Thank you. Now, we could use another player. How are you like to come to my office and try it out? Try out what? What are you talking about? A good game for some real corner. No limit. You inviting me? I'm inviting you. Sorry, I just dropped by for drinks. Well, have it and then come on up. No, I didn't bring any money. Thanks, just the same. Well, your IOU is all right with me. I know you're good for it. How do you know that? Well, you've been in the business a long time. You know, you know what happens to a guy who can't pay his way. One year begin. Right away. Let's go. And now, in connection with the AutoLite family charity drawing, here's another actual interview as recorded in Greenwich, Connecticut. How do you do, sir? And may I have your name? Michael Spazano. I understand that you just signed up for the AutoLite family charity drawing. Is that right? That's right, sir. I have. And why did you sign up? Well, our local school needs an addition. And if my name is picked, I'll designate them. I think that's wonderful. And the best of luck to you, sir. Thank you. Why don't you help your favorite local or national charity, too? It won't cost you a cent, and you have an opportunity to be among the twenty-five persons selected. Remember, there's nothing to try or buy, nothing to write or solve. All you do is visit any AutoLite family, car dealer showroom, and fill out a registration form. Your entry may be worth many thousands of dollars to your local church, hospital, or other pet local or national charity. So why not visit one of these car dealers, DeSoto, Hudson, Plymouth, Studebaker, Dodge, Willis Nash, Packard, Kaiser, or Chrysler. There's not much time left, so sign up tomorrow. And now, AutoLite brings back to our Hollywood soundstage Mr. Richard Whitmark in Elliott Lewis' presentation of The Card Game, a tale well-calculated to keep you in suspense. It's simple. All you need is the right night, the right game, and the right cards.  And you do it all just right. And when you're finished, you're on your feet once and for all. You've got a pile. And then you give it up. Yeah, that's the way to be cured. But you don't know that's why I came here tonight. Do you say no? You don't know anything. I spread that deck out there. Let's see who deals. Come on, come on. I'll introduce you to the boys. Fine, sure. Boys, hey! This is Chickeregon. Bill Archer, chick. Hello, Bill. Hi. Lou Humboldt. Lou, hi. Well, make yourself comfortable, chick. Thank you. That's nice to have some fresh blood in the game. Yeah. Take a card. Oh, sir. Queen. Great. Tim. Well, what do you know? Ace. You deal? Oh, here you are, chick. That's $10,000 worth of chips. Is it too steep? No, no, no. Fine. All right. End the up. Well, good luck, everybody. That's how I got here. That's how I got into this. That's why now I look into different faces from the games I'm used to. These guys aren't kidding. They're cold about it. Hard. But if my luck changes, I'll be in business. I'll have everything we ever wanted in all our lives. I'll take it to her and I'll show her and I'll say, Jenny, Jenny, here it is. I won't gamble anymore. I'll beat a wall. All I need is one hand. Just that one hand. A thousand. A thousand? OK for me. A thousand? A thousand more. Kind of frisky tonight, aren't you? Are you going to stay in? I mean, your thousand and a thousand more. Here we go again. Six. Queen. Want it for your check? Yeah. I get a king. Ace bet. Let me check my whole card. 500. And another. I will, too. You can find it out yourselves. Everybody else, huh? OK, here we go. Ten with the queen. Another ace with the ace. Oh, God. Well, what do you know? A king with my king. Bet the aces? A thousand. Here are faces of a pair of kings showing. I gave up a long time ago. Jim. With a pair of kings, I have to see one more card. And for you, deck for me. You still bet? Ten thousand. Ten, huh? Ten. Take it. It's beginning to happen just the way I hoped it would happen. There's nothing that can stop me now. I'll beat them all right off their feet. Because the cards are coming to me, coming to me. And they can't do anything about it. In? You in? Oh, yeah. I'm in. Me, too. Your calls. Pretty aces. Pretty jacks. Two pair, huh? Yeah. It's too lucky for me. I might have known. What do you say, chick? I've got three queens, Jim. Let's see them. Three pots and a row, chick.  Just like that. Take your money. Hey. Hey! Bring us a new deck. This Regan's getting too lucky. I said, oh, get good and sore. Fumble around the rest of the night if you want to. It's okay with me. Because the soreer you get, the better off I'll be. And the better it'll be for my wife and my kid. So keep getting sore. I like it, Mr. Sando. I like it. What time you got? 5.30. What do you say? Shall we call it a night? I'm all for it. How about one more round? Chick has been awfully lucky. I like another crack at him. Okay, chick? Or do you want to cash in? I'll play one more. I kind of thought you would rather walk out. You thought wrong. I'm winning. That's right, kiddo. I'm winning. Not me. I've dropped 13. I've had it for tonight. Me too. You hear that? They don't want to play anymore. Well, in that case, I guess... How you like to double that part? Sure. Sure. I'll drop in tomorrow. Right now. How much you got there? 42,000. I'll cut you. High card. Take all. I love Jim.  There you are, chick. All or nothing. High card. You don't have to... Let him make up his own mind. You know, I had an idea. He came in here tonight to see how far he could go. I don't know exactly what he had in mind, but he's always been a two-bit player. I still think he is. And I might be wrong, but I want to find out. Are you a two-bit player, Reagan? Put your chips out, chick. Pick a card. You first. Sure. Eight. Beat it. Well, that's tough.  That's tough, chick. But better luck next time.  No hard feelings. No. I'm not even a one-bit player, gin. Huh? My IOU is no good. But that car of yours... It isn't my car. I haven't got a dime, santo. I've been playing with your money all night. And you was gonna walk out of here with it, huh? Yeah. You hear that? I heard. Yeah. Sorry, chick. You knew what you were doing. Harry, get the door. I wanna remember what they did to me. But I'm going to as much as I can. I woke up in the morning, and I was lying in a ditch next to a highway outside of town. No one would give me a lift because of the way I looked. But somehow I got home, and Jenny was gone. And that's really what finished it for me. They'd taken her to the hospital while I was with santo. And Jenny had had our baby. What happened to you? I'm all right, honey. What about you? I told you. I told you I'm fine. And our baby boy is fine, but your face. Who did this to you? What were they? Some people I tried to steal from, Jenny. What? I got in that one game I've always been talking about. I had it. I had it all for us, enough for a house, enough for the kid. You were right, honey. It was just another steak so I could try for something bigger. I lost it all, Jenny. And I lost it while you were here having the baby. Well, I learned my lesson. This time I'm cured. No more gambling. Jake, are you sure? I'm sure, darling. We did. Take me back. Yes, darling. Then I won. Didn't I? You won. There's only a few days left to sign up for the AutoLite family charity drawing. Only a few days left to name your favorite local or national recognized charity to receive thousands of dollars in cash. To enter the drawing is as simple as this. Listen to an interview recorded in Elizabeth, New Jersey. This gentleman just signed for the drawing here at Murphy Brothers. May I have your name, sir? John Grenette. When you entered, just what did you have to do? All I did was sign my name and he addressed. Did you have to buy anything or try anything? Not a thing. Just fill out a form. Well, thank you very much and the best of luck. You're welcome. So don't wait any longer to sign up in the AutoLite family charity drawing. You may be one of 25 persons selected and you may have the great satisfaction of designating your favorite charity to receive a big share in $100,000. Visit any or all of these car dealers, DeSoto, Hudson, Plymouth, Studebaker, Dodge, Willis, Nash, Packard, Kaiser or Chrysler. Do it tomorrow, sure. Next week, the true story of a man who alone was responsible for most of the methods of police detection in use today. It's called the Bertillon method. Our star, Mr. Charles Boyer. That's next week on Suspense. Suspense is transcribed and directed by Elliott Lewis with music composed by Lucian Morowick and conducted by Lut Bluskin. The card game was written for suspense by E. Jack Newman. In tonight's story, Kathy Lewis was heard as Jenny, featured in the cast were J. Novello, Hi, Everback, Clayton Post, Eddie Fields, Luke Krugman and Joe Granby. Richard Widmark may currently be seen in the 20th Century Fox production Helen Highwater in CinemaScope and Technicolor. And remember next week, Mr. Charles Boyer in the Bertillon method. This is the CBS Radio Network.


He's speaking too fast. Now look, you may be nervous up here, right? But I want you to do this, okay?   Can you push some power behind him? Hey man, I get it.  Matter of fact, reach out and say, man, I get it. I totally understand, right? But slow down.  Make sure they know at that moment when you're going to close that deal, there is nothing more important in this world than them, okay? And you feel what they're saying. You understand it and you feel it. However, there is a better way. And I'm going to go over it with you. And I'd like right now to be able for you to choose me as your trusted advisor. I see hundreds of people make decisions on vehicles every single month in here. And do you know what? I wish every single one of those decisions that got made was on a vehicle with a little more miles on it. You know why? Because they're just machines. They're machines in the mileage on them, whether they're 40,000 or 70,000, they're all going to drive to half a million miles. So what's the difference? Money. Now, at the end of the day, is money important to you? Yeah. So you get a lower sell price, you get a lower monthly payment, you get a vehicle that drives like it's got 10,000 miles on it, which isn't that all that matters? It's safe for you and your family. And then when you go to trade it back in in a couple of years, do you think cars become worth more or worth less when you own them, worth less? When the depreciation hits, you don't get stuck in that tornado cycle. Isn't it nice? I wish everybody had the opportunity to buy a higher mile vehicle like this one, but they don't. You've just won the lottery. Now I switched it on you. Okay? I want you to understand something.  I told and I didn't say what he said is that I know that it has high miles on it, but let's go ahead and still do business. What I'm going to say is, aren't we lucky that it actually has a little more miles now that we've discussed it? And I wish everybody had the opportunity to buy a vehicle with a little more miles so they could save the money that you're about to save. It's just not available, but you guys, for you, it is. Isn't that nice? Boom. Listen to me. Who creates the atmosphere in which the customer lives in? The salesman. What's it called? It's called, it's called transfer and motion, making them feel the way you feel, but it's called buyer management. Who's the buyer? The customer. Who's the one that is responsible for managing them? We are. It's our job to manage the buyer. Listen, I need you to understand this. Do you know why this is called the master closer seminar? Because it's not called, I want to sell car seminar, sell more car seminar. Okay.  Why? Because I want to show you that your people need some guidance. Okay. I do too. When I go to buy shit, it's just, I can't find it.  Nobody studies the art of speaking. Nobody studies the art of working through with strategies and conversations. Why?  Busy equals broke. Productive equals paid. You guys want to get paid? You know what you got to do to get paid? You got to become the best in the world. I want to tell you this, you guys have a whole another level and you're seeing it already. How do you get it? Simple. Pay close attention today. Take tons of notes. Stay dialed in. When your mind starts to swerve, guess what? Knock it off. It's swerving that got you where you're at right now. Okay. We stay on the straight line path to what we want, the fastest road. Okay. And by the way, listen to me.  I know you are, man.  But guess what? You just got called out in front of all these people. And guess what? No, no, no.   Can I go again? No, no, no. I'm going to get you in a little bit. I'm going to get you in a little bit. Yeah. I've already gave you the answers. He's like, can I go again? No. We'll hit you later. Hold on. I'm going to rotate out. Good job. I'm going to rotate out. Do me a favor. We're going to, I'm not going to stay on this one. I'm going to flip around it. By the way, real quick, can I go through this? Pull the word track up real quick. Let me show you something. So everybody here, I'm going to show you something. I need a coach here. Where's one of my coaches? Who can say this word track? I got it. Okay. Hold on. Here's what I want to do. Everybody watch this. Just watch this real quick. If you want to know how to learn, how did you learn what you said? But, and then you wrote it down and you studied it, right? Okay. You see that? I like the car, but the miles are too high. If you want to know how to get good at it, right? Memorize it. Can you guys memorize shit? When you move houses and you go to a new home, you have to memorize the new address, right? Like it's just a little bit weird the first couple of weeks, isn't it? Right? Watch this. Ryan, don't look. Andy, I completely understand the miles are too high. But look, if you were to buy a vehicle with less miles, you end up spending more money. I mean, look, if you took this 2012 Dodge Ram with 80,000 miles and you found the same exact truck with let's say 40,000 miles, you drove both those cars for three years. Which one would you end up owing more money on when you went to trade it in? The one with less miles. I mean, look, our vehicle having a few more miles, the largest part of the depreciation cycle has already been taken into consideration. So when you look at the bigger picture, you won't be upside down in our vehicle or the other vehicle with less miles you most likely will be. So at the end of the day, going with a highly rated vehicle like ours with a few more miles is definitely the best decision if you want to save a lot of money right now. And later on, when you go to trade it in, baby, let's go. Hi. I love to spin at the end. Hey, here's my deal. I'm going to ask you this question and I want you right now. We're going to move to these quick. I want you to ask yourself this. Are you battle tested?


Okay. Can we begin? Woohoo! Okay, welcome everyone to the... I think the last panel for today, thank God. It's been a long day, but an amazing one here at Wikimania. And this is the last panel about wiki data and education. So this is part of the education space, and what we want to focus more today on is how do we basically utilize wiki data and implement it into the curriculum and use it as an educational tool. So to do that, I've assembled such an awesome group of people. I'm really excited to have all of them, either in the room or online. This is basically almost everyone in the world working on wiki data and education. So I hope you're as excited as I am for just hearing these guys. And lady. Okay. What we're going to do, I'm going to put this down for just a bit and just give you a really brief intro of what to expect today. So we're going to go and do a really short introduction so you'll get to know the panelists. And then we're going to have basically a really hopefully fun and enjoyable discussion together about what we can do in education wiki data. And hopefully then we're going to have some section where you interfere and talk to us and let us know what you're doing because maybe I'm missing someone who's doing awesome work that we don't know about. So please let us know. This is about community building and really trying to bring different types of work that has been done around the world. So you have a variety of experiences and a variety of people you might connect to different people doing different types of work. And this is about, I don't know, creating some inspiration and some hopefully good will of other people in the community to start work on this and implement. So without further ado, let's do the round of introduction. And we will start with Martin and I'll just, each person will present themselves. I'll come up here and... And yes. This is present mode so hopefully, yes. Go for it. Hello everybody, I'm Martin. I'm the Wikimedia and Residents at the University of Oxford and I've been delivering a workshop called Sparkle as a Foreign Language and I deliver this to library professionals and data professionals and to the public to people who are entirely new to databases. And this is some information about me. This is a knowledge representation system that knows about some things. It knows about three things that are human beings. Two of them are a married couple. We represent the two properties. They have a child and there are other entities of other types. So this clearly isn't a representation of all people or even all people in my family. So lesson one, every knowledge representation system is incomplete and we're querying the system rather than querying reality. It has names. So I call that someone... I used the name Bob for my father and you might have a... know someone called Bob and you might use the name Bob but you mean a different individual. So what do we learn? Bob is a name that has different meanings according to the context. You could say that the meaning depends on the name space. We can see that these are statements and we can ask questions and a question is always an incomplete statement. So Cindy has child who. And it's as simple as that. We don't need the architecture of acronyms and RDF and Spark and so on just to get people thinking in nodes and arcs and thinking in terms of data and writing queries. And Martin works at the University of Oxford specifically at the library. Do you want to say something about that? About your residency in general? So yeah, I'm based in the Bodleian Libraries at the University of Oxford which is a copyright library so it has a complete collection of everything published in Britain. 13 million items as well as special collections but I'm based in this library within university advising museums, advising researchers and also involved in some public outreach and doing things for the public as well as the data. And in this Martin in a way represent one of the most important ties I believe in outreach, especially in education and that is the connection to libraries and so your work and also you and who you'll meet in a second is also doing some work with libraries in a bit of a different way but I'll let you and present himself and thank you Martin. Hello. We're standing up then I guess. So my name is Ewan I work at the University of Edinburgh as the Wikimedian in Residence. I've been there since 2016 and the university sort of has really tied this in with digital skills training and information literacy and data literacy. Trillions of pages to navigate terabytes of structured and unstructured data to navigate we're looking to support students in Scotland growing digital economy and create a workforce of about 100,000 young people who are data literate in the next 10 years. So I've worked with in teaching and learning with data science for design masters students for the last couple of years looking at ways of working with real world data sets because that's what their course leaders wanted they wanted the students to have that real world experience and that's worked really well for us in the last two years we've now employed a Wikidata intern for last 10 weeks and she's coming to the end of her internship in the next two, three weeks so that's probably enough for going on with. Yeah and Jun is here because I personally wish that every university in the world would have a Wikimediating resident and I think the work that you've been able to do as a Wikimediating resident and also Lane right so that's a really cool again very different so again the diversity but we'll get to you in a second again the diversity of what positions such as Wikimediating residents at a university can achieve especially with Wikidata today I think is astonishing and you'll hear some more examples of the really incredible work that you are able to do so Lane you're up next Am I? Yes you are now How do you do? My name is Lane Raspberry I'm a Wikimedian and resident at the School of Data Science at the University of Virginia in this role I share information from my university Wikipedia and especially Wikidata what's new with Wikidata is whereas before it was there's a culture of difficulty and trying to make it useful for students to edit Wikipedia this is something that many people in our movement have tried to do for years for various reasons it's much easier to design programs in which students at schools will engage with Wikidata this is because perhaps whereas when people edit Wikipedia they're doing it either to learn the information or to share the information something different about Wikidata is that you can actually edit Wikidata for the sake of editing Wikidata or rather for the sake of learning about data management so that makes the nature of Wikidata programs in universities much different from any Wikipedia program in any university I've got another slide so in addition to presenting Wikidata to students in different ways and running different kinds of Wikidata programs another reason why universities might want to care about Wikidata is because Wikidata is a channel by means of which a university can show off its special collections and also its research output through the Wikisite project or different profiling systems like this one that's my university are developing Skolia a front-end by means of which one can browse the content in Wikidata Thank you so much Lay and we're gonna go to Zhuo next and I'm gonna just shrink the slides because I actually want you to see Zhuo so give me a sec here I learned this trick now wait a second here it is can you see Zhuo well enough Hi everyone yes yes so I'm very happy to be to some extent to which many I couldn't come this year and congratulations and the panelists and everyone in the organization team to having made this possible so a reason that I'm part of this panel is because I've been experimenting with Wikidata Professor at the School of Journalism in São Paulo, Brazil and I'm also a member of the Wikipedian Education User Group and the Brazilian User Group and I normally understand my experience with Wikidata to make stand as an attempt to ease or make more efficient my work with Wikipedian Education which I would call an instrumental use of Wikidata but of course there are other people from Brazil the panel that can explain more what we've been doing that is really more ambitious and looking to Wikidata as a fundamental resource for education so I understand I only have one minute and a half and I hope I'm okay on time no you can actually expand a bit you want to say something about the labs just as an intro okay well I think we'll talk about the labs but what I can say about one program is and I put the slide on the deck which is a use of Wikidata associated with Wikipedia to make and create entries for elections in Brazil one thing that we understand in Brazil is that people and they've been really better at that they vote poorly and one understanding of this decision of voting poorly is that they have very low quality information this is associated to the fact that they forget to whom they voted so now for three years I've been working with my students to create entries on municipal elections in Brazil we have 3,000 cities in Brazil around 3,000 cities in which there were effective elections and this was mainly done on Wikipedia and though it was having a huge impact so in every election around 500,000 at least people were going on to Wikipedia to look at this information my students were understanding this work as boring which was of course a problem and the part that they displeased the most was to create the tables of the elections so with Eder who is on the panel and Eddie Cazzilini who understand also gave a remote presentation to that depends on to mirror statistical information on the Wikipedia entries so that when the students go to write their entry and I can show an example later there is a full template that already brings all the statistical information so who got elected how many votes they got understanding of what an entry is so they can focus on what is more meaningful for a human edition so this is a work that I wanted to highlight which is I don't know what you are saying but I think this is the template and you can see an example I'm trying to open the Mbable thingy just to show them how it works so you just open the blue thing the Wikidata item and it will generate a sequence do you want to generate an element just run but I have an already published example if you go back to the slides and you have an example so what you are going to see now is completely Wikidata generated and it gives the students the possibility of working on the sections that are more interesting which is the historic understanding of the election the dynamics of the campaign and more importantly for what I am focusing on the analysis of the outcome of the election and so through this mechanism we have basically covered all election all the last elections in Brazil we are moving backwards now and this is something that is multilingual this is Wikidata so you can basically do the same thing on your project if you think this is interesting create entries on Brazilian elections these ones we guarantee but you can of course adapt this to we have worked on from an experiment in education to do stuff on your own language or your own culture so this is the example I wanted to highlight and the information is on the slides connected to the event wonderful Eder do you want to continue hello my name is Eder I am from Brazil and I oh ok and I have been helping João in the project of education behind the scenes so I am the tech guy so I make a lot of work in Wikidata and I help Giovanna in her partnership with Museo Paulista Glam and I helped Erika to write the code for this tool I also made other tools that help our projects like Tiglum and other templates so always I am very curious about how do we get the tools that we have available for us and make a creative solution of a problem that we are having so for a long time I was the only person able to do that in Brazil and I was working with our user group so when I was getting not tired but over doing things and things were starting piling up we and I came with the idea of creating the Wikidata labs this is a series of trainings like Wikipedia and Commons mainly so we have now 18 Wikidata labs done and a series to continue about structured data on Commons about how to edit on Commons about ontologies on Commons and a lot of little things that help us understand Wikidata and how do we implement Wikipedia and this is for other projects like education programs that Joanne runs Wonderful, I think everyone should have an editor but that's just me Hello everyone I'm Giovanna Fontanelli I'm also from Brazil like half of these people here apparently I'm a journalist and I'm a historian and I work with the Wikipedia in residence in Museo Paulista there I do a GLAM which is like what I like to do the most I'm also studying to be a teacher and a professor so I like education very much yeah so I'm also participating in a course from the WMF education course that is going on right now but I also participating in this project which is in English is poking Wikipedia in Portuguese Wikipedia Falada which is a project that we made audio descriptions of paintings for people with hearing impairment and also I participate a lot in the Wikidata Labs I gave two one about open refine and the other one about Wikidata projects I don't know if I'm forgetting about something but I also help organize those Wikidata Labs and that's it I think if I'm not forgetting about it and the reason that I ask I ask both well Joao, Edder and you two there is I think really each of you bring a bit of a different angle to it and also because the work done in Brazil has been amazing this year and I think the labs are completely innovative and such an amazing thing that's happened that I really wanted to give it a voice again and for people to get to know it so I'm super happy you're here you specifically I think give a voice to another intersection of education which is the connection between GLAMS and education do you want to say something about that? I'm actually working with this project called Some of All GLAMS which is about bringing institutions heritage institutions to Wikidata so we can use those tools that they to show the MBABEL and also the Extracted Info Boxes from Wikidata so we can help those institutions in their not just their collection but actually the information about the institution put that in Wikidata so we can bring them to Wikipedia in a more Extracted way and this for me like GLAM for me is not like it's all about education I don't know why it's so separate like here in Wikimania in space because for me it's the same thing I think for some of us and that's one of the reasons why I really wanted you here because I do want this voice represented and I would just want to add two things just for reference and give credit to Richard Nipple who's been experimenting with Wikidata at the Met and who's created the first MBABEL or BABLE he called it MBABEL and then you kind of adapted it and expanded it so I love when that happens this is exactly the case of collaboration in the global community and why it's so important for us to kind of know who's doing what what are we working on this is why we created a community called we have a Facebook group called the Wikidata community where we share things and collaborate and this is just one platform for many others but any collaboration is important because one builds on the work of others and all these tools are a result of his work and I would also like to mention Mike Peel who kind of inspired some work just by the fact that he was in Brazil for some time so the collaboration with him and João also sparked something that sometimes being the right person at the right time could create a whole new splurge of ideas and projects come to life and on that note I'm going to move to our last panelist which is Will Kent Will Hi can you hear me Hi Hi thank you for joining us from San Francisco Yay So hi everybody my name is Will Kent I'm the program manager at Wiki Education for our Wikidata program which we just started so we just wrapped up two courses this past week they're all Wikidata courses they're six week long virtual synchronous courses that meet for one hour each week and we also offered one day long in person workshop this summer as well we started with targeting librarians as the course participants but worked with people from museums nonprofits and companies as well and the courses were really really fun I loved facilitating them it was great some of the course participants had experience with Wikidata before others were starting from scratch which presented a really good challenge in developing a curriculum that worked for all of these different learners and I'm proud to say that everyone was able to edit which was very cool but before they edited we put them through a training process with seven training modules that detailed community policies norms, how to edit, how to create properties, how to query and how to use a series of Wikidata tools and I just want to share some results of the course with you so I think this is the next next slide if we can switch it Yes I will move to your slides give me just one second Sure Has it closed? I really hope no Here it is Perfect so there are three courses we worked with 38 participants and over these six weeks we were able to create we were able to edit 2,500 items in about 9,000 edits we created 230 items added around 800 references there was one person who was really good at merging and we merged 1,300 items which is crazy and we created one new property from scratch and on this slide I've attached our dashboard pages which is the tracking tool that we use at Wiki Education to look at all the results of these courses so feel free to follow these links whenever you want to see all of the items that we were able to edit and we'll be running more courses in the fall and really really happy to be working with these new communities of editors and bringing them on board into the Wikidata community Wonderful, thanks so much Will and just a quick question before we continue the modules that you developed as part of the dashboard are they well are they part of the dashboard or it's an external thing that you created for the students We created them for these students but it's part of the dashboard so it's open for everyone to use and we'll be refining these trainings moving forward so keep an eye out for them and feel free to use them as you like Yeah, so if you're not familiar there are basically two dashboards I know it's a bit confusing there's the one for WikiEd that's the original dashboard and there's the one that called Programs and Events Dashboard at the rest of the world not the US Canada program is using so what Will is talking about is in the WikiEd dashboard which is important because it's a resource they literally built structured instructions for students that some of you may be able to use so that's important to know Thanks so much Will and you're probably wondering why I'm here as well and why I actually wanted to have this battle so I presented myself and I said my name is Shani but I didn't mention that I'm an educator and I work at Tel Aviv University and I've been teaching with Wikipedia and Wikipedia projects for I think six years now since 2013 I basically opened in 2013 an elective course focused on so a semester long course dedicated to working on Wikipedia and Wikipedia projects and students can choose to take this course they study with me for a semester and then continue with the other stuff that they need to do but this has been an amazing experience and what happened since 2013 when I opened my first course the first course was focused on medicine so it was called WikiMed in med school two years after that when we saw it was successful I adapted the course model to the whole campus so the new course opened the rector's office at the university approved the course and so basically every undergraduate student at Tel Aviv University can today elect take an elective on Wikipedia and Wikipedia projects why is it important because it's been a few years back now that I've been experimenting with talking about WikiData so in the first year I only gave one lecture about it and that was it like the intro to what is WikiData it exists know that it's out there right as part of the semester long course on the second year I became Greedier and I kind of wanted to do more so I did the intro session and in another session to teach the students how to query on the third year I became even Greedier and I felt it's not enough so every year that's passed I felt like I wanted to do more and more and more and the students wanted more as well so last year I succumbed and I completely changed my course the one for the whole university and basically opened the first course in the world to feature WikiData properly and today the course is basically half Wikipedia and half of the semester only WikiData stuff so I haven't been able to do what you in Brazil have been doing like having 18 sessions that's like a dream for me but I do have 6 or 7 sessions with the students per semester so that's good can you hear me? yeah so I want to share about how we can use this new platform it's also part of my PhD research I'm literally researching these days how we can use WikiData and utilizes for educational purposes and what happens in this new interaction with this new platform because we have so much research in education about how Wikipedia can be used for educational purposes and when I talk about education I don't only mean in the classroom but as a life long learning process there are all sorts of things that happen when we interact with Wikipedia but no one yet has researched what happens when we interact with WikiData so that's one of the things that are interesting for me very much and why I was so keen to learn what's been done by other educators and Wikimediants around the world so I've decided to assemble this panel basically to have a chance to chat with friends about the importance of why we should all be doing this and why everyone in education should care about WikiData because it is the future and I think Martin you and also you and you mentioned data literacy which is the future and some educational institutions so this is something that we need to kind of give or prepare our students for and I know João has been very much conscious of that as well in his work with his students so yeah that's why we're all here basically Martin do you want to Mike so yeah I originally wanted to train people on WikiData and advocate WikiData but now I've converted to seeing it in a broader sense that WikiData is the ideal platform to get people understanding databases and knowledge representation these basic concepts without that weight of knowledge that say I had learning so you look at the standard book or the learning materials for course in databases of course what people do and it will say imagine a company with a lot of voices to its customers customer A bought products one and two customer B brought product three and it's the most boring stuff and you think well if I have to learn this from my job I will I'm not going to be excited but if you're doing something about WikiData you say well let's do it about the dynasties of China and the emperors let's do it about Marvel superheroes let's do it about women who've won the Nobel Prize and the dates and the prizes they won that's people's interest so you say we're going to learn about databases in general we're going to learn this language Sparkle which is a language that you can ask questions in you can get answers in any topic and you go to that and they are going to learn inevitably that WikiData is really cool that it will get stuff questions relevant to their hobby or their job or their interests and so I'm looking to just get people thinking in that way so my session start with pen and paper we're not even on the computer I say I show there's my family in nodes and arcs triple representation you describe your family on pen and paper to your neighbor you saw a film you enjoyed the film describe the key characters in the film their relations to each other just to get people thinking in terms of triples and queries and like acting out the query the query service and the person asking the query you say like Cindy has a child who and then you look at the graph what would answer who in that question so I don't have the sort of political or health or witchcraft applications in mind it's just whatever that person is interested in in their career or their interests but WikiData as well it's content it's the ease of use of the query service and the way it helps you it's the multilingual nature that people adjust the interface to their own language so yeah mass data literacy is something we need and WikiData has come along as kind of the ideal tool thank you so much you do want to add something about that so myself and Neveno Evans who's not here but he's the brains and I guess Sean's the brains also but behind Hystropedia we developed three workshops as part of a conference session at Repository Fringe so we were sort of developing a workshop on how to teach sparkle querying for the layman how to do mass upload how to do manual editing so we did three workshops of the course of the day and we had people from different repositories different backgrounds come in and say look this would be brilliant we need to teach this to our students what you're teaching is actually sort of the idea of how information is structured where information comes from the provenance providing a verifiable provenance that you can actually query and actually feel empowered to do something about you can make maps, you can make queries you can make it personal you can upload pictures of your local home town area it's very well yes we're quite lucky at the university that we had a rather good database that detailed the witch trials in Scotland from 1563 to 1736 and it was lying static in a Microsoft Access database since 2003 and I just approached the researchers and said could we import this into wiki data and basically we find it's the perfect tool for engaging people in understanding data literacy and in understanding the period people are motivated to learn it's like what Martin said about moving away from a dry teaching of the subject into something that really empowers and motivates people to understand and want to be involved and that's our experiences that people feel that this is something they want to be a part of before laying continues I'd like to acknowledge that we have a younger generation joining us this is probably the youngest wiki data contributor that I know and I would like everyone to say can you say hi to everyone in the audience hello everyone okay thanks for joining us we appreciate we in education and wiki data give a place to the younger generation to join as well so awesome to have you with us Lane I would appreciate a few words from you because I think you at your university have been doing such unique things as well in terms of research and also not only data literacy but tapping into research and things that are a bit different than what everyone else is doing so can you say a bit more about that before we delve into our actual questions for the panel there's all kinds of applications for wiki data and certainly at my university we've been using it to teach basic introduction of data management and data science also surfacing special collections from the university so if you have some not so long ago it was very difficult to do distribution and dissemination of databases certainly in a meaningful way with visualizations and what do you know wiki data you can pull out old data sets circulate them in wiki data and give them a new life so I think that any organization that has an interest in wiki data they're going to be engaged in these kind of activities something else that you can do supposing you have computer science students statistic students students who are studying machine learning and they want arbitrary projects to do again they could you could train them on accounting fake accounting data sheets this seems to be the standard by means of which many people have learned or you can send them into wiki data and you can if they need a student project and a client so this often happens that students work with a real world client to do a project over a period of time either three months or six months in my university these are nine month projects they're going to be masters level students so with a typical commercial client or a corporate client they're going to meet with the students from time to time and some clients will meet more with the students and give them more real world experience some clients will meet less actually the wiki community will meet and talk with you to the extent of your attention and concentration they never get bored it never sleeps if you want to talk about wiki data and the attention span never runs out and then in fact that's what students at my university have done so my university is hoping that these graduate students studying data science year after year can do different wiki media based projects pulling information out of wiki data having a real world impact working with the wiki media platform as a corporate client perhaps in coordination with somebody else so two projects I'll just mention them briefly one of them was looking at misconduct on wikipedia the hundreds of millions of instances of misbehavior that someone can get engaged with in wikipedia and try to detect those more quickly with whatever information you can get from wiki data and wikipedia another is academic research matching go through any academic journal by the tens of thousands or hundreds of thousands and try to match those two wikipedia articles where the information from those journals perhaps from a given university's collection you could prefer one university over another to integrate that into into wikipedia for distribution and dissemination awesome so I think one of the important things that you're just talking about is how we kind of need in order to succeed and the way I'm stressing it is just in parenthesis that it's so clear to you we are looking at all of you as potential clients because you're like agents the moment you leave this room you'll be expected to go to your universities and try to to do it yourself so we'll try to help you and give you some ideas from our experience on how to do that no pressure but one of the things that Lane is basically saying is that we really have to know our audience really tap into what they need, they want and one way to go is tap into audiences that are sort of low hanging fruits so yes if you have universities or research all sorts of research organizations that basically look for databases that already work on machine learning computer science digital humanities all of these fields always look for databases so start with them start with librarians because they are our best friends as you know in education start with people who are passionate about it and know what it means to actually work on wiki data and with that I'm kind of assuming that you understand that it's not easy right and so my real first question to the panel would be to describe some of your difficulties when you just started out all of us were able to kind of create something start out something and that involved persuading the organization we work for that this is worthwhile this is something that we need to pay attention to and to to put our energy to towards and so how did you go about it what did you do to persuade your institutions to care about wiki data and what tips would you give to anyone trying to start out then we'll talk about the difficulties after securing that first like putting your foot in the door as we say but yeah let's begin Erica do you want to start to yeah why not okay I don't know I think the difficulty in the beginning when I started the project with for example was to establish a way to make things work in a certain way like how we received metadata and how we would make it in a good way to put on wiki data like the best way we could do it to put on wiki data that's why we get in touch with you don't know quick statements and refine for example later but you're talking about the fact that they already agreed to do something and give you and I'm talking about the one step before so how do you persuade the museum to give you it was actually very easy with them they actually want to be part of it that's what I meant like it was very easy for me at least the process was already it was very exciting when I got into the project the difficult for me was to establish this contact with them like how it is the best way that you can give me that information so again one of the takeouts from here would be for me go for again the low-hanging fruits the ones that already want to give you their data if you go to an institution and they have some metadata but the institution is not willing to give it away then look for someone else start with a partner that actually wants to collaborate and I think you and you had an experience getting the databases and again you had a willing partner do you want to add something about that well I mean like advocacy has a long tail I would say so we've been going to events where people are like that we would want to contribute and just talking we've not been trying to create those events we've been going along and speaking and then people have approached us after the event so it's with this we at the universe we created an open knowledge network as well we try to link up all the people that were working in open knowledge open data and just bring them along and have some talks so it was a mixed approach and once we'd sort of showed off wiki data to a course leader who was in the room who has written a paper about how teaching of data science required the use of real world data sets he was like okay I want to create a project within our data science program called the data fair where we get 13 stakeholders into the room from local government from other universities and they just pitch their data set to the 45 masters students in a sort of 3 minute speed dating session if you want one and the students have to pick a data set that they are wanting to work with out of all these 13 problem holders and tell an engaging story with over a 7 week period so it's getting them to understand the data work with the data and then tell engaging visual visualizations with the data by the end of a 7 week period that comes with its own challenges in wiki data context because you have to get them to understand how to get enough edits to make mass uploads in the first place and then understand the data modeling or maybe even suggest properties that they need to create but the data fair really worked as a model and on other courses since taking it up as well, they see it as a way of getting the students to personalize and tell engaging stories with it that they've got a visible output at the end of it.  It takes time, it takes community building and network building and you plant seeds most of which you don't know when will sprout if ever and it just takes time so it's not as easy I mean to come to a point where Erika was your next Martin I but to come to a point where where Giovanna can basically go to a glam and they will just give stuff to her that means there was a relationship built before that so don't expect it to be easy on the other hand it doesn't have to be that difficult too so just keep it real and keep trying until you find the right partner to work with but it does take time Martin do you want to say something about that? I just want to share a couple of tips because of course getting started off the wiki prefix causes a problem people who are library professionals or data professionals and you're talking about a wiki something anyone can edit that's a bit scary why should our data be on that so two ways around that one is to say because they are kind of forward looking I work in an ancient but forward looking institution and people aware that linked open data is the future and sparkle query language is the future so I say I'm going to teach you about sparkle using wiki data you can mask it or you can make it positive you can use an analogy that people already familiar with web so the web isn't something anyone institution controls you can't control what is said about your institution on the web but it exists and you've got to be visible there it would be insane not to be visible on the web and this we are building the semantic web the web of open data is the next generation of the thing that everybody will be on it would be insane to choose not to be on that so it's an analogy of what people already accepting helping them accept something that's new and unfamiliar and a bit scary completely, João do you want to add something sure so going back to your point about community involvement I think it's a key aspect of transitioning from wikipedia education programs to wiki data education programs which is to one extent we've learned a lot throughout the years and we've documented a lot on the uses of wikipedia for education but then we had to justify to our institutions and to our communities that we were starting starting something else and I think that was a real challenge because it created a risk and we were moving from something that was still meaningful that people still wanted to do that we knew how to do to something that people didn't really understand they didn't know they could do and that we didn't know how to do it and I think this panel and the overall idea of documenting experience advancing research having technical training has been a real asset to develop this kind of transition when we envisioned the wiki data labs for our community the idea was to provide a sense and the background that anyone can add meaningfully wiki data and this was happening amid the crisis on wiki in English that was of course reverberating until wikipedia in portuguese in which the projects were having real political issues and our sense was ok we have an opportunity here to actually create common ground for everyone who is a wikimedia at large to take advantage of wikipedia and wiki data and actually integrate the project for the institution I think a real challenge was ok now I've convinced you for the last 5 years that we should do wikipedia and now I'm gonna tell you that ok we are still gonna do wikipedia but now our focus is gonna be on wikipedia and for them I think it was a challenge I think the times that I had in which I had the meetings with my directors or with directors of institutions that we were working on the challenge was we had nothing to show we were just starting and we said ok now we've been good at doing wikipedia now you trust us we are gonna be good at doing wiki data and we are a lot on us but we need to move way beyond what we've been doing about documenting and publishing on the users of wiki data as a resource for education I've showed you the instrumental use of wiki data when I do a wikipedia program now I think we really need to understand how we can make a fool and I was very interested and I'm gonna check what we can do what are the learning points for a wikidata education program full-fledged and we need to have a conversation because it's not intuitive students at first they don't like it at least from my experience their experience of wiki data is like they are filling out a form and they don't see it reverberating to anything which is completely different with wikipedia wikipedia you edit and you get what you edit so they see it and they have the impact on wikidata one impact that is not there is view so you edit a lot and you don't have view and there are real technical challenges to having someone who never did any wikidata to start doing I don't know which statements at a very high level and this is a real challenge for us we are moving into this direction and it was an issue for the negotiation of wikidata program it was based on trust to some extent I said ok we know you do we can understand we have no clue what wikidata is but with trust you can actually do something with it and I think a challenge for us is we need to publish we need to document and this is something that we are still failing to do at this point which is understandable but so on the one hand we're just starting out but on the other hand see how much easier it's going to be to all of you because there's already examples of cool things that you can show institutions that you interact with and you can show the examples from the UK and from Brazil and from the US and I think that's valuable building this global community and saying to an institution it was just like when we started out Glam or education before wikidata the more info we have of what's happening in the world and being able to say hey you know Oxford University is really investing in it and the University of Virginia is doing amazing things with research and why are we not part of this party so this is something that's really helpful to doing it but I think in that sense all the difficulties that you're describing exist especially when you want to scale but in some cases it could be easier and I think Mayan Lane's experiences were a bit easier at the beginning but in my case it was easier to go to create a course about wikidata just because I was already in I already had a course that was about wikipata I was already I've already convinced the University that there is value in Wikipedia so all I said is hey I want to change my curriculum a bit and nobody really cares enough to really check what I'm doing and the students like it they give me good greetings and that's it I'm just doing it so that was my case so in that sense if you already have educational programs where you already teach Wikipedia or it doesn't have to be a program it could be any type of initiative use it to teach something else and it could be something really small just expose people to the idea and that's always true even when we do Wikipedia trainings or we want to start something new we need to give them something small and manageable that they can see the impact of I completely agree and in Lane's case Lane do you want to share a bit why it was a bit easier in your case same reason that's what you heard right here is that there were people who are already interested in wiki at my university before I came there they were already ready to engage and open minded frankly I feel sorry for the person who goes to a university not knowing anyone wiki engaged and trying to convince someone to do anything with wiki data my experience has mirrored everything that you've been hearing here so there's a lot of diversity and then trouble with documentation and wiki data there's a lot more documentation than there was in the past there's still so much documentation to be done I definitely think that it gets easier even week to week things can change a lot in wiki data because people continually put out documentation it might not be worthwhile to list challenges nowadays so wiki data changes quickly I agree that some of the challenges might change or might be fixed but I think there are some challenges to outreach that are always true and they are also kind of universal it doesn't matter if I'm in Brazil or the UK or Israel or the US I will be always facing faculty manager policy makers that don't get it don't understand what I want from them I've never heard about wiki data or the semantic web what do I want from their poor lives so it comes down to finding the right partners and the right spaces to kind of spread these ideas and I can share that one more thing that I've been doing besides opening my course and part of my work at the wikipedia education user group has been to try spread the word in our circles and what is our circles the open education field in general there is a huge community for open education and there are good partners many of them are concerned with OERs open educational resources and for some weird reason that is really beyond me those people don't see what our community produces that is weird because we are the biggest OER in the world so part of the process for me has been to recognize that and start to do this outreach that we all do step by step conference after conference chat by chat to start letting people in that community know hey what we're doing is actually OERs we are part of the discussion and hey you should really care about wiki data because that's the new cool thing that everyone should care about so go to conferences and present it and show cool stuff from all of these you're all in my presentations by the way so just know that your work gets to all sorts of weird places in the world without you even knowing Will you've just been in a position in wiki ed where you've expanded your you've been doing really well right you're one of the most established education programs in the world thousands of students every semester in gazillion places in gazillion right I'm exaggerating but you know the wiki ed the U.S. American the U.S. Canadian program in education is huge it encompasses really thousands of universities and lecturers and students every semester Will if you can say something about this transition because it took you some time to kind of do that and how did you approach it and how did you find your first partners to start with great question so the wiki data program is just a couple of months old and what we did to find partners was well we knew we wanted to work with librarians because there were some publications that came out that specifically identified wiki data which is an interest and a way forward for librarians to develop their skills and so what we did is we attended conferences we sent out emails we tried to partner with library organizations in the U.S. and then showcase a lot of these wonderful examples that this panel here has helped develop to convince librarians that this is something that they need to do and in working through these library associations we thought we would be able to get the attention of department heads and co-hosts of university deans to support librarians and teaching them how wiki data works and so that was the approach that we took and once we were able to get them in a room and start having conversations around wiki data it's very easy for them to get inspired and see how exciting this all is so whether it's identifying content gaps or seeing what the power of queries can be or how they can share their collection with the world that message really resonates with librarians so it's just a matter of at that point getting them excited and showing them that these tools aren't impossible to use and lowering the barrier of entry which is why we developed the training modules and why we decided to make it a multi-week program so we could create this space where people could learn at their own pace and come to real humans with questions and have them be answered in real time and we found that to be really helpful and also develop their own community of new wiki data learners I think that was really useful for them to kind of support each other and then also obviously to receive the support of the community I relied on everybody here in this panel to inform what our curriculum should be and I can't tell you how many emails I've exchanged with people and phone calls we've had about what's important, how should we frame it who should we reach out to and we found that this approach has been successful so far in engaging with librarians Yeah and this kind of brings me to the second question to the panel of what works for you because in a way I totally got what you're saying Will when I started teaching wiki data in my courses like it was four years ago I think there was no precedent, no one was doing it yet, we had no we were really just trying to for the first time engage with students in a classroom and trying to bring the idea of wiki data and it was hard, it was like it was ten years ago for wikipedia in academia where we had no experiences at all of how to do it how to go about it, how should it work what should we teach, how exactly to divide the time, what should the session look like, how do we make it interesting and engaging to students and so I'm kind of wondering what works for you, like how do you construct, do you have any tips for people here in the audience or people who are hopefully listening from around the world to how to construct your sessions to be effective and what has worked for you in your collaborations so Will because we're already with you, do you want to share a few tips? Yeah, absolutely so what I found has been the most successful approach is to let the participants drive the conversation you know you can frame wiki data or at least one aspect of it but in working with subject area specialists I tried to make as much room as I could in these courses for them to pursue what they're interested in so I had librarians who were specialists in chemistry and others in biology and one example that comes to mind is there was one reference librarian I worked with who was especially interested in the criminal justice system in the United States and he just ran really deep into wiki data and learned how to query really quickly and realized that there was no data model around anything that didn't involve convictions so there was nothing about there's nothing about like pardons or exoneration which for criminal justice at least in the US is a really big deal and it's very common for this information to be missing and so I created this space for him to explore that and work on a data model and he's now since proposed it to the whole project chat and is very involved in working with other wiki data on this project and there's a property proposal going through the property proposal process and this was all within six weeks so he was somebody who had a little experience with wiki data and in that short amount of time just by giving him space to pursue what he was interested in is now developing all these models and could result in multiple properties so I think that's a really good idea and then letting students or participants drive toward their interests is really a helpful approach. Wonderful that's basically a question to anyone so whenever you want Martin. I just want to extend that and say in an educational context you've got the opportunity of making a virtue of all the difficulties all of the real world data headaches the modeling difficulties the inconsistencies I'm working somewhat with Emma the witch finder general her project and she's had the problems of different real world data sets and it's ambiguous what links to what and she's had to use different tools at spreadsheets and open refine and a bit of Python coding and it's been headed calendar models these things have come up and now she's incredibly employable she's done all this that somebody in a real world situation in a job would have to do all this and she's done that in the context of a short project so make a virtue of that that's great incomplete documentation good so people just have to experiment and just put different things into the database and see what it does that's a great learning experience and one of the things I have to add because you didn't mention it and I kind of know this about your work one of the things that I really loved about what Martin is doing is that he has been exploring ways to tell stories with data and I found that very inspiring right and do you want to maybe mention a few things like your ass-relay bit I was talking about the difference between the way I learnt databases and I learnt the SQL query language and you have company A and company B and in voices are in a table and companies and customers in a different table and it's really boring and I linked a model a semantic web model like Wicked Data is but many other platforms are as well that's a web of interconnected things it's not tables it's things that have properties and relationships and those relationships model things in the world and sometimes those relationships are surprising and unexpected and a relationship can be informative like Lord Byron the most famous romantic poet was the father of Ada Lovelace the first computer programmer that's quite an amazing thing to take on board and because we're dealing with a web a giant web of interconnected things we can look at things in terms of web so I showed my family there or a bit of my family in terms of notes and arcs and I said it could be anything else it could be Marvel Superheroes it could be the Wilson craft family or the Shelley family but the different knowledge about a thing in one of these webs is the sum of all the pathways to and from that thing and so we've got a natural human way to go between this representation in a database and what we talk in language about how we have a journey through this one point Cunningham's law and Godwin's law are related because Cunningham invented the wiki and wiki is the field of work of wikimedia foundation and wikimedia foundation employed Mike Godwin and Godwin invented Godwin's law and a lot of the great kind of educational materials that people have made whether it's books or documentaries or so on are somebody's personal pathway through a set of information so there's a series that's very influential in the UK when I was a kid called Connections and it was kind of this polymathic presenter who would and it's all on YouTube and he would take you really surprising things so he'd show you a particular kind of way and say this is connected to supersonic flight and well it's not but then he'd show you the historical things or the connections and so you learn something new by connecting what you already know so we have the opportunity to do that in a very graphical way with wikidata queries and yet connecting historical things with studying or connecting the fictional mythical folkloric things we're interested in or like ourselves so just tapping to what you're saying before going to João I'll add that you've mentioned graphics well I think this is one of the strong suits of wikidata especially considering what João was saying before that it's very difficult for people to see the impact and I think whenever I talk to anyone it doesn't matter who students any stakeholder that I'm interested in harnessing I will use visualization in wikidata to kind of show the power of wikidata and what Martin described practically for example was a session that I did with my students right I told them with the help of Martin he facilitated from afar so that's another thing that I always do I try to bring friends from afar to my courses and have the students meet different people from the global community I think it makes it interesting and kind of helps them understand that this is a global thing you're not only in your own small context but in any case that was in parenthesis Martin's idea about how to tell a story in relationships and seeing visually has become a practical exercise that I could do in my class with my student that was practical right in small groups I divided them to small groups in class and each chose a subject that they were interested in and had to represent it and create a visualized example of the relationships and again one of the takeaways for me at least is have something very small that you can showcase for at the end of the session so students feel like they have actually conquered something that they did something practical and they can show for it right Joao do you want to say something about that and then Eder because you've been teaching a lot and I'm so interested in hearing about your experience okay so yeah so I think one thing that I've learned from my experience is that the collective work is more engaging than Wikipedia activities so the experience that we've had that I've had with Wikipedia was okay so I have a student and this person is going to work for one term on researching everything she or he can on one particular topic but then this work will be to some extent fragmented so it was hard to create the sense that they were so I have my I have very large classrooms I have around 700 students per semester so I really had a hard time to make them discuss or understand that they were doing something collectively and one project that we run was okay so and this is the power of data I mean aggregate data so each one of my students went to take pictures of monuments in Sao Paulo city and as they were taking the pictures they were including the image on the Wikipedia item and improving the Wikipedia item so the first thing that happened okay so we were I was basically sending my students all over the city and the first thing that they saw I know if I can share my screen here see there is a button here can you see my screen okay so this is the entry on Sao Paulo and Wikipedia in Portuguese and if you go to the last section external link you actually have a link now of a map of monuments and sculptures with data so the first thing we did as they were producing we were mapping their production and this was so you can if one of these dots as you know is a Wikipedia item so they were feeding information and we were having a map that not even the city had these are the pictures basically taken by a student of my minor Mike Pio I don't know if he's in the room but he was in Brazil and he's a terrific photographer so he was helping on this project as well and one thing that we realized by doing the map is that around 300 monuments had gone or disappeared and the city and I was teaching journalism and the city government didn't know what happened to the monuments either they were destroyed robbed and we started an investigation with the students to understand what had happened for each one of the cases so we moved from producing data that we were able to aggregate onto a project and ended up with one I think there were three students publishing a natural journalistic piece on disappearing monuments in the city of São Paulo and I think at this point this particular example I convinced my cohorts that wiki data was powerful because there was at this point no other way of doing this and of course then there was the organic use of the wiki data the wiki data feeding that was happening so Mike Pio at the time was developing the common info boxes that you can see for instance in this category so the students were seeing that ok so this is a monument in Brazil it's about a painter, it's not beautiful but it exists, it's documented it's part of our cultural heritage so it should be taken care of and so they were seeing all this happening and then we generated listeria least some wikipedia of monuments and it created a momentum and it was really powerful and I think this is one way of dealing with it just to put Giovanna back on the spotlight this is exactly the way that we've been working with GLAMs so when we again my students asked them to do audio files of painting this is a project that is done within the scope of a large initiative with the museum which is the Museo Polista and it makes sense it becomes meaningful for the students to have their work replicated to understand the process of wikidata but we haven't been able and this is me particularly, I haven't been able to really have a meaningful section on data literacy through wikidata it's just something that as a professor I wasn't really able to make them understand and I've seen just from my last term some comments of my students saying ok so I understand wikidata now which I thought was a very bold statement from some of my students basically because I don't understand it myself so they understood wikidata but they said I don't really understand at this point how it relates to data literacy and the way that data modeling, data ontology digitalization has been taught in journalism has not been in the spirit of wikidata which is again a challenge that I should address and we should address at some point Thank you Ed, we kind of have to start to wrap up so I do want to hear from both of you Edor and Giovanna just you have been teaching through the labs so these are targeted sessions in a way and what are some of the takeaways that you can give to the audience in terms of what worked for you and what you've been doing to kind of engage your students and make some you are a tech quiz, you're known for it but not everyone as moguls who don't really know queries that well it could be really difficult something that we have to kind of confront because sometimes we may talk to students or faculty who really knows they have a background in programming or computer science in one sense or the other or two librarians who have the thinking like they understand triples they understand ontologies so there are some markets that are easier for us to work with but they are not and they deserve a chance of wiki data as well so we kind of have to adjust our teachings to different types of audiences so what have you been doing that worked for you right I think knowledge as we had on learning days knowledge happens when you have the conditions to learn so what we did was we had the conditions based on our audience that so if we have librarians we will study their language and adapt our courses or wiki data labs to their understanding so they can they have the opportunity to learn how to teach something like that in some extent as how to talk with their students about the topic that they are trying to understand on wiki data so this is the main thing we focus when we are presenting on wiki data labs something like that Giovanna I have a different kind of answer actually I mean when we are teaching in school we have this kind of difficulty for example to teach young people math because sometimes you can make them understand why they are using those numbers those formulas and everything and I'm going to stop here and start another point I started my life the week movement as a student from juan when he did I think it was the second year that he made that project it was three years ago and I was in love with it like with the commons with wikipedia but when I started to work with him I stayed because I understood the way that the information was structured I got to know wikipedia then I didn't know before so I think what is the challenge here for students is to understand why wikipedia is so relevant like in this mysterious platform that almost no one knows really about it but it has the power to structure wikipedia which is huge and they know wikipedia it makes sense to them to know the extent of wiki data but they don't that's a way to make them understand that's why I talked about math for example because it's hard to make them understand why math is important if you don't show the results so how you do the math you know thank you, yeah when Joao told about the student or in you about the students that sometimes know more than you try to get the student to wik in universe so when I started working with Joao I was only producing images to illustrate mathematical concepts on commons so one day he sat with me and then oh you need to program in Lua to translate this module to Portuguese wikipedia so I love the theme so I started to learn by scratch and then I work with this today so when a student is really passionate or say I understand wiki data try to bring him or she to the movement and then make them teach the best way to learn yourself Lane closing remarks if somebody shy about wiki data for whatever reason so we're talking about persuading people to engage with wiki data it's more important to plant the seed than actually get a convert so in the case of wikipedia people have been familiar with it for their entire lives sometimes they give it an edit and then they may or may not become a regular contributor but people who do become regular contributors it's often after being asked several times perhaps over years same way with wiki data I go to the people who are more interested in doing something immediately don't push the people who aren't ready and also I know that every three months these days wiki data is developing quite rapidly it's going to be a different beast in three months and much more friendly so the documentation the tools, the visualizations that you can show people the more data that goes in the easier it is for more people to put their own data in there so just tell them what it's about and let them go at their own pace Ewen, last remarks it's sort of reaching a tipping point I think because we've got so many good use cases now there are more things we can do in terms of making nice and easy resources which will change over time we've got a lot of great video tutorials of the little easy to do stuff like OpenRefine which Ewen has been amazing at producing so thank you for that and Sean's helped and Nav's helped with that as well and Simon and Jason and Martin of course so we're producing these resources just like making them available to people where they can access them and using WordPress sites to sort of like have created little tutorials but we're producing a booklet of case studies now in the UK of how Wikimedia projects are being used in education we've got 14 case studies in the UK and I've got my one Wikidata project in there but maybe it's time we had a sort of Wikidata in education booklet and we pulled all these stories across the globe together and started handing these out to people obviously we'd need to make it a digital copy because things change quite quickly but yeah we could print these out Martin, last remarks just adjusting are we going to make people into Wikidata contributors long term I never try to tell people what kind of hobbies they should have but there are people with a particular kind of mind that loves detail and loves representing things and loves knowledge representing and those kind of people become researchers or librarians and so on and I think once they see Wikidata they won't be able to help I know people who've like it's become a big thing in their time because they want to see what maps they can make and what they can find so we don't even have to tell them to do this as a hobby the right kind of obsessive we'll seize on it and on that optimistic note I want to thank you've been an amazing and patient audience thank you so much we don't really have time for questions but we're all here for a few days more and when this video is out the audience is very much welcome to comment and ask us questions there is going to be Wikidata conference in October so we will we've talked about changes we'll see where we are in like three two three months but I just want to share my own last takeaway to go experiment this is a super exciting time in that sense because we're just starting out exploring this we have Wikipedia around the world it's very widespread now educators are joining us in masses but not with Wikidata so you could be the first in your community to do it and to explore it and we're a nice bunch of people so connect to the community and enjoy because it's just exciting what we can do and thank you so much for my panelists especially João and Will for joining from afar we appreciate your efforts and keep inspiring me thank you so much