Washington calling David Harding counter-spy. Washington calling David Harding counter-spy. Calling Washington. The makers of male pouch chewing tobacco and Kentucky Club smoking tobacco present David Harding counter-spy. In Washington, a very prominent bachelor by the name of Harold R. Blake stood in the center of his expensive apartment at the time 1125 at night. A dim light burned on a small ebony side table. Mr. Blake stood there. His face was ashen white. Great beads of perspiration stood out in his forehead. Go on.   Go on. Go on. It's a suicide, Mr. Harding, a wealthy bachelor in poor health.  But remember this, Blake held a very critical post in our government who were still at war and trained enemy agents are still at work and their experts at making murder appear to be suicide. Are you going to investigate? Well, I think I'll at least ask a few questions. The note Blake left that he was committing suicide because of ill health. I think I'll phone his doctor and see how bad his health really was. You would know, Dr. Wolfe, if Mr. Blake was in poor health. It's a big suicide from everything I know of us in perfect health. Thank you, doctor. That's all I wanted to know. Goodbye. Peter, I think I'll go over to Mr. Blake's bank and see if his finances were worrying him. No, Mr. Harding, Mr. Blake's finances were in perfect condition. He had many government bonds and securities and I'd say he was worth close to a million dollars. Thank you. Peter, when a man commits suicide, it isn't his health and it isn't finances. Look for the woman. Exactly. That's just what I want you to do, Peter. Pick six men and turn the city of Washington inside out. But find out what woman Harold Blake paid special attention to. All right, Peter, that ends that lead. But meet me later. I'm going to take a long chance now. This is the accounting firm which Blake employed to handle his books. Why, yes, Mr. Harding. We have been the accountants for Mr. Blake's firm for 11 years. Uh-huh. Mr. Stanton, I want you to go to Mr. Blake's office and get his office pad of appointments for the past year. Notice, especially on the list, lapses of time and compare those dates with his personal check. I want to know what personal checks were made out during those periods of absences. This should tell us where Mr. Blake was during those absences. Mr. Harding, we went over Mr. Blake's office pad and found he was absent from his business during the past year on four different occasions, a week at each time. Good. Now, during those absences, did he make out any personal checks? Yes, he did. The absent corresponds with the check he made out at the Saratoga Hotel, Saratoga Springs. During Mr. Blake's second absence, he made out several checks at the clubhouse, Pine Hill, South Carolina. This third check, it was made out at the Hampton Towers Hotel, Atlanta, Georgia. Uh-huh. The accounting of those personal checks certainly shows where he spent his time when he was absent from his office. Thank you. Saratoga Springs, 31, expensively dressed, unusually attractive. Full report follows. Well, this is beginning to get very interesting, Chief. Yet, we still may be on a wild goose chase, Peter. We have no idea who that girl was. You may have been perfectly all right. But how can we ever find her? Well, here's a little something I dug up. Hmm? On April the 2nd, Harold Blake made out a check to the Washington Jewelry Company for $8,000. Look at his sizable amount for a bachelor to be making out to send to a jewelry house. That's what I thought, Peter. We're going to check that jewelry house. I want to guess what Harold Blake bought with that $8,000. He bought a solitary diamond ring, it for whom he was buying the ring. That's all. Thank you. Well, Peter, now we know Mr. Blake bought a ring for some woman. But who's the woman? Where is she? That's a tough question, Chief. Well, let's put two and two together and try to make five. An $8,000 ring, that's an expensive ring. Check all insurance companies, Peter, and see if within a few days after April the 2nd, any woman insured a ring for approximately $8,000. We're getting somewhere, Chief. Here's the insurance report on April 5th. A solitary diamond ring platinum setting was insured for $8,000 by Miss Adele Winston. Hotel Belmont, Washington. Winston, Adele Winston. That's right. I have a report out of here. Age, 32, height, 5'5", light hair, light complexion. Educated in France and Switzerland. Apparently above the coast. Hmm. The woman seen with Harold Blake at the resort was dark. Dark hair. Don't wait for a parking lot if it's the same. Both apparently wealthy, both smart dressers. Peter, there's something wrong about all this. Something very wrong. The problem has got to be approached from some unusual angle. You have a very beautiful apartment here, Charlie. Oh, thank you, Mr. Harding. Hi, Colonel. I'm here to make a very unusual request. You knew Harold Blake personally, didn't you? Oh, yes. Was his suicide a bona fide suicide? Perfectly candid, Colonel Higgins. I don't know. And that's why I've come to you. I need the aid of a citizen, Colonel, who has a very important governmental responsibility. You're 48. That's correct. A bachelor. A confirmed one. You're a very handsome man. Oh, come on. Very fascinating to women. Hold on, wait a minute. Oh, this is serious business, Colonel. As I understand it, when there's to be an invasion of the Pacific Islands, at the very last minute, special charts must be supplied to all freighters. So they'll know the little harbors where they'll have to unload the supplies. Yes, that's correct. Naturally, months beforehand, I have lists of such harbors, so those charts can be printed at the last minute. That's why I'm so careful of making new acquaintances. I have a responsibility on you, Colonel. That's just why I've come to you. Do you happen to know a Miss Adele Winston? No. No, I've never happened to meet her at the Harding, but she's a very gorgeous woman. Anyway, she's usually surrounded by any number of admirers. Well, I'm going to arrange for you to meet her, Colonel. I'm going to ask you to make yourself just as interesting to her as possible. In fact, I'm going to ask you to try and make it even a constant attachment for a time. Oh, no, Harding, that's a little too much. Oh, not at all. Colonel Higgins. You could be the principal factor in possibly exposing one of the most clever spies in this country today. You don't think Adele Winston was in any way connected with Blake's suicide? You don't think she's acting as a spy? That's just what I want to find out, Colonel. Good Lord Harding. Now, I've arranged with Lady Collin to give a reception next Friday evening. I've given her a list of guests that she's to include. Miss Winston will be one. I'd appreciate your being another. Casually meeting her. Actually, Harding, under those conditions, no man could refuse. I thought you'd feel that way, Colonel. Now, after you meet her, please don't try to contact me in any way. Leave it up to me. Find out what you're doing. That's not the description of Adele Winston. J9 aboard New Orleans Limited reporting to Mr. Harding. April 16th, 4.20 p.m. The Colonel in question boarded train at Washington. Four later, joined woman on train. Dark complexion, expensively dressed. Very beautiful and exotic. Weight about 110 pounds. Tickets read at Atlanta, Georgia. Peters, who is meeting Colonel Higgins on these trips? Is it Adele Winston or who is it? And that girl has been meeting Colonel Higgins. Good Peters, what did you find out? You were right, sir. After the girl got off the train with the Colonel, I went into a compartment. There were unmistakable signs of dark-colored face powder. They were also traces both of blonde hair and black hair. The black hair had definitely come out of a wig. Apparently, the girl must board the train, get a compartment, and change her appearance before she comes out and meets the Colonel. And it is Adele Winston. Because he's been absent from Washington at the same time as the Colonel has. Probably to explain to the Colonel that because of her prominent standings, you must disguise yourself. The Mr. Adele Winston must have been a mysterious woman when the Harold Blake went off on trips before he was murdered. Without a doubt. He's connected with his murder in some manner. And now, right now, he and the Colonel are down at Virginia Beach. Very much like the Harold Blake setup. That's what I'm afraid of, Peters. And we've got to make sure Colonel Higgins isn't killed the same way. Back to David Harding, counterspy in just a moment. But first, male pouch is one of America's best known and most popular chewing tobaccos. A popularity and preference built on uniform, consistent quality and delightful taste and flavor. On or off the job, male pouch is a favorite because chewing serves to steady nerves. And male pouch helps to ease the tension of hard, strenuous work. Helps to relax tired, tense minds and bodies. Male pouch is delicious and satisfying, full of pleasure and long-lasting goodness. Because it is made of choice selected tobaccos, properly aged and skillfully blended to an exclusive formula. Next time you buy chewing tobacco, buy a supply of male pouch. Reach yourself to the best. Now back to David Harding, counterspy. What a beautiful sunset Adele. Yes, it is beautiful. I love to lie on the beach after all the others have gone on. Yes, it is nice. Boy, sometimes I see a look come over your face. Anything troubling you? No, dear, nothing. That's that you're worrying about your responsibilities. Those charts you will have to have made before Japan is invaded. No, I don't think I am. Oh, I wish we didn't have to go back to Washington tomorrow. You must, though. I've got some important conferences. Oh, here, put these in your bag with you Adele. I'm afraid I'll lose them in the sand. Well, why bring keys out of the beach? Well, I don't dare leave them in the hotel room. This little flat key. It's quite an odd shape. What do you think? It's the key to the secret cabinet in my library where I keep the confidential charts. Oh, no, Roy. Don't give me the keys. It's too big a responsibility. All right. I'll hide them next time under the rug at the hotel. Let's go in the water. Come on. All right. I'll beat you to it. Oh, just try. Well, it's looking for you, Harding. Oh, hello, Colonel Higgin. Glad to see you. Sit down. Thank you. I thought it might be better for us to meet openly at the hotel dining room rather than me to go again to your apartment. And good many things have happened since we last talked, Harding. Yes. You've proved yourself a veteran. A trained counter spy agent couldn't have done better. And this isn't about the Harding. Well, I think the big moment's here. It's now or never. All right. Just give me your orders. I'd like to have you invite Miss Winston up to your apartment Wednesday night for an informal dinner. Just you two. Oh, try. You don't think through you, Harding. Miss Winston is a spy. Yes, Colonel. I do. But, Harding, I kept the key to my secret file where she could get it. Even called her attention to it. Harding, tell me the truth. You don't think Miss Winston was the girl who was with Blake on those trips before he committed suicide? Yes. His blood is on her hands, Colonel.  Now, Winston, I'd have to you've had dinner. I wish you'd go with her into your library for coffee. But, under no conditions, Colonel, drink the coffee. I'll casually drop in a little later. You intend to break her Wednesday night? If I can. I hope I can. What's the matter, Colonel? Why doesn't she? I'm all right. Is this something? No? What is it, Colonel? You can tell me. Harding. I've fallen in love with her. You don't mean that. Yes. Yes, I do, Mayor. I love her. Good heavens, you can't. But I do. I think she's innocent. She didn't even try to duplicate the key. She's never even tried to ask me questions about secret government affairs. She's a murderous Colonel. She double-crossed you in a second. I didn't realize how lonely I've been. She's so clever, smart, beautiful. Everything about her. You're not thinking of doing what Harold Blake did. No. No, not that. I guess I can see it through. I'm sorry, Colonel. Terribly sorry. Well, I guess there's no more to be said. We'll go through with it tomorrow night, as planned. Good. But remember, do not drink any coffee that's poured. I wish I could say something, Colonel Higgin. I feel for you from the bottom of my heart. But this is bigger than you or me. I know. But still, I think she's innocent. The more champagne, Adele? Please, sir. That's a stunning evening gown. Very flattering. Thank you, sir.  Oh, yes. Oh, and James? Yes, sir. Mr. Adele and I will have our brandy and coffee in the library tonight. Yes, sir. And I'd like to have you remain this evening. Very good, sir. Oh, by the way, Adele, did you ever know Harold Blake? Blake? Was he the man who committed suicide about four months ago? Yes. Well, I guess I don't see him. I don't feel I have anything. An awful thing, wasn't it? I think there's some talk that it wasn't suicide. Oh, rumors, I suppose. Oh, like silverware? You got me looking at it all right. Yes, I do like it.  Now, it seems so insignificant. To have something really worthwhile, you've got to have someone to share it with. I've found the same thing too, Roy. Closeness and comradeship, and more than anything. So I have James serve the coffee and brandy in the library now? Yes, I've seen it. I'd rather sit on the divan with you.  And then you'll say something so tender. What a woman should be a mystery to a man. Adele. I know. If you don't know how much. Yes, I do, Roy. My heart's been acting the same way. Then that isn't that last trip. Do you love me, Adele? Yes, Roy. Very dearly. Oh, I... Pardon me, sir, but a Mr. Harding and his friend have called. Oh, yes. Yes, James. Show them in, will you? Yes, sir. Why did they have to come at just this moment? Well, Harding's a very good friend. I guess he's just dropping in. We were on our way to the club. I'll reach stop in, Colonel. Oh, I'm glad to see you, Harding. Have you met Miss Winston? No, I don't believe I've had that privilege. Good evening, Miss Winston. Good evening, Mr. Harding. Miss Winston, there's a friend of mine, Mr. Peters. Good evening, Mr. Peters. Pleasure. Colonel Hagin's Peter. Good evening, Mr. Peters. Won't you join us in brandy and coffee? No, thank you. Well, sit down, gentlemen. Make yourselves comfortable. Thank you. Well, while you men chat, I think I'll go and freshen up a bit. Well, we'll miss you. You men stay here and talk. I'll be back in just a few moments. Harding, it can be true. That woman is innocent. Colonel Hagin's, ordinarily, I wouldn't operate this way. But I owe it to you. Tell me, Harding, tell me. She's really one of your agents working with you. Tell me that you suspected me and really did this so she could check on me. Tell me that ease my mind. Colonel, would you ring for the butler, please? Oh, yes. Yes. Harding, it's the butler you're really after. Tell me it's he and not Adele, isn't it, Colonel?  I sympathize. But I can't change the facts. Now, don't say anything for a minute. If you ring for me, sir... I believe the Colonel wanted you to pour some brandy for me, please. Yes, sir. Thank you, sir. This is a gun in your stomach. Don't move. Harding. Sorry, Colonel. Put the cops on him, Peter. I've got him set. And he's the man you're after, not Adele. I believe he... There. Without his wig, he looks more natural, like his pictures. Your butler, Colonel Hagin's, is Victor Strautz, one of the cleverest international spies. I have not. I don't know what you're talking about. Colonel, you might be interested to know that when your former butler was hurt by a car six weeks ago, it was a plot. The man at the wheel of the car which ran down your butler was this man standing here. He closes the butler so he could get into your house here. We've been checking him for weeks. Here you go. Drink this coffee, you serve the Colonel. You don't like dope coffee, eh, Strautz? Please, Harding. Adele's coming back. What'll I do? Watch her when she comes in. I think she'll be pretty surprised. Is that your butler? He's here. Yes, Miss Winston. We removed his wig. Why? For the reason he happens not to be a butler, but Victor Strautz, a very noted international spy. A spy? He's a spy? Yes, and quite a catch. Well, I'm so glad you caught him. Peter, take Strautz over to the other side of the room. That's it. Come on, you know. Come on. Lord, you must feel terribly about this taking place in your apartment. Here, take this brandy.  Thank you, Adele. Thanks. Colonel Liggins, I have a recording I'd like to play for you. Would you mind my using your machine? Well, I know. No. The switch is right on the side of the radio. Thank you. Just a short recording. I feel a little dizzy.  What kind of a record is it, Mr. Harding? What's the purpose of it? Well, I believe it'll be self-explanatory. It was made last night. There. The voices were speaking rather softly at the time, but I'll turn on the full volume so we won't miss anything. The papers. Colonel told me he kept them in the wall safe in his library. If we don't find them, you'll just have to keep on playing. I will bend his face every time I get near him. Well, the plans aren't here. We failed tonight. They'll probably put them in here tomorrow night. Well, I hope tonight would be the plan. We must break him and force him to commit suicide the way Blake did. If he won't, we'll poison his carpet. Ow! Not so quick. You dog! You fathom!  Adele Adele, what are you saying? Fortunately, Colonel Higgins, the plans weren't there. If they had been, you wouldn't be alive tonight. Adele, they'd gotten them Adele Winston would have taunted you.  She'd have told you she'd gotten the plans. You'd be disgraced. She'd have broken your heart to you'd have done what all the other men have done she's worked on.  Colonel, her real name is Gattasthenia of Hungary and perhaps of Japan, a paid spy who goes to the highest bidder. And that man, Victor Strautz, is her husband. No. Take them all the way, Peter. The other agents are out in the front hall. I'm awfully sorry, Colonel. I had to expose the whole plot in front of you so you'd never have any doubt. I didn't know a person could be hurt quite this much. You may have saved the lives, Colonel, of thousands of our men. I hope so. There have been a lot of people who've given all they had in this war. Hello, Colonel, through the window. Return veterans coming down the street. You've done them and men like them a great service, Colonel Higgins. No one will probably ever know about it. But you will. I will. I know. And Harding. I'm glad I was able to help. The war isn't over yet. Every one of us has still got to sacrifice. Some one way, some another. I guess this way. In just a moment, we'll tell you about next week's counter-spy case with David Harding. But in the meantime, Mr. Pipesmoker, once you get that white burly Kentucky Club flavor in your pipe, you'll have the sweetest pipe you ever smoked. There's no other tobacco quite like white burly Kentucky Club when it comes to keeping a pipe in good taste. For Kentucky Club is a mild, free-burning smoking tobacco, pleasant and inviting from the first puff right down to the bottom of the bowl. It is mild the way you want it, yet is fully satisfying. It stays lit and burns clean, leaves your pipe sweet and fragrant. The white burly tobacco blended the special Kentucky Club way truly makes a difference. That's why Pipesmokers everywhere agree that quality and smoking pleasure considered, it is truly America's number one smoking value, a value that challenges comparison. Smoke Kentucky Club pipe tobacco. Treat yourself to real pipe smoking enjoyment. This is David Harding speaking. Of the hundreds of counter-spy cases handled by my office, I feel the case we're going to dramatize on next week's program is the most unusual. It concerns probably the smallest thing ever sabotaged. So tiny it is invisible. So important that potentially it may affect every single one of us and be considered one of the great discoveries of all time. And invite you to listen. Wednesday, June 20th, same time, same station. David Harding, counter-spy. David Harding, counter-spy is the Phillips H. Lord production for the male pouch tobacco company of Wheeling, West Virginia. Don Low speaking. This is the blue network of the American Broadcasting Company.


Welcome traders to the Tick Mill weekly market outlook for week commencing the 29th of November with me Patrick Munderley So starting with the US on Monday we get October pending home sales last print minus 2.3 percent We're looking for a median estimate of 0.7 percent 7 percent on an expected bounce back after September's surprise fall. We'll also see November Dallas Fed Index last out 14.6 looking for a median estimate of 17. This will provide a timely update on manufacturing in Texas. We'll also get Fed Speaker Powell as you speak along with Williams, Hassan and Bowman. On Tuesday Prince of Notes are going to be November Chicago PMI last time 68.4 expecting a 67 print supply disruptions and Delta are ongoing issues. We are also looking for November consumer confidence index last out 113.8. Median estimate for this print is 110 confidence to be held back by Delta concerns and again we will have additional Fed speak in terms of Chair Powell and Yellen who are before a Senate panel and we'll also hear from Williams and Clarida. On Wednesday November ADP employment change last time 571 K looking for a 515 K. Another strong result is expected we're also going to receive October construction spending last out minus 0.5 percent looking for a plus 0.5 percent this time and expected lift to give strength in terms of the housing demand that we are witnessing in the US. We'll also get November ISN manufacturing last out 60.8 looking for a 61 print this time. Manufacturing has been strong despite the supply and Delta concerns and again we will have Fed Chair Powell and Treasury Secretary Yellen this time on Wednesday they'll be before the House panel. Coming into Thursday we look for initial jobless claims 199 was the last print don't have any don't have any estimates for this one initial claims are at multi decade lows though and then wrapping all up on Friday we have the all-important November non-farm payrolls last out 531 K looking for a market median estimate here is 500 K but risks are tilted to the upside momentum in employment growth is expected to persist at this stage we'll also get the employment rate last out 4.6 percent market median 4.5 percent edging the unemployment rate just that bit lower we'll get November hourly average earnings last out 0.4 percent looking for the same again really 0.4 wage growth to remain steady given the labor market we will also get November ISM non-manufacturing print on Friday last out 66.7 looking for a 65 here as the Delta headwinds likely to persist we also get October factory orders last out 0.2 percent looking for a 0.5 percent print this month as expected to advance further given transport and source is a likely source of volatility there we will also get October durable goods last out 0.5 percent no market median the final release X transport orders point to a solid fourth quarter of investment and wrapping up Friday we will hear from a Fed member Bullard who's speaking at a Missouri bankers association so from a technical perspective the dollar index traded up into the to test the offers just below the 97 handle saw some supply come in late Friday now looking for a three-way corrected move to complete into testing the 9450 from there anticipating a bounce really at this stage only would get materially bearish the dollar if we take out the ascending trend line support coming in at around 9350 and that would suggest that this current corrective phase is complete and the downtrend is resuming but for now we stay focused on this 9450 test and watching how price responds there heading into the eurozone let's run through where we're at in terms of data on Monday we get November consumer confidence last time minus 6.8 persistent inflationary pressures and Delta resurgence are likely to keep the consumer constrained we also get November economic confidence last out 118.6 looking for a 117.8 print via the market median estimate this will present clear headwinds to confidence in terms of this resurgence that we've seen in coronavirus concerns over the last few days on Tuesday we'll get November CPI percentage year over year 4.1% last out looking for an improvement here 4.3% inflationary pressures expected to persist really into 2022 now on Wednesday we will get November market manufacturing PMIs last out 58.6 looking for a similar print this month for the final release and then heading into Thursday we get the unemployment rate for the eurozone last time 7.4% looking for something similar here the fall in unemployment expected to become a little bit more gradual now and then wrapping up on Friday we get November market services PMI last out 56.6 looking for a similar print this month final release for the month obviously and then we get October retail sales last print was minus 0.3% we're looking for a plus 0.3% versus the market median here set to lift as demand even out really between goods and services similar dynamic to what we've seen developing the US from a technical perspective the euro dollar traded into that 112 support zone now looking for a three-way corrective move to try and get us back up into this this 115 former resistance sorry former support to now act as resistance really at this stage only a loss of of last week's lows at the 111.80 would would set up a bearish decline to the downside to test that 110 weekly trend line that I referenced in the weekly live trade analysis session last Thursday so for now we're looking for further upside and see if we can grind it out to get a test of that 115 area in Japan pretty light in terms of data next week what have we got we've got on Wednesday November Nikai manufacturing PMI last out 54.2 and we're likely to see a similar print as the final release for the month and then really it's it's just on Friday we also get the November Nikai Japan PMI services 52.1 expected and that again is the final release for the month dolly yen took a took a beating on Friday along with the risk aversion that we saw I'm now looking for a three-way corrective move anticipating to find resistance back into that 114.50 area and we're going to look for a test of the major ascending trend line support back into 111.50 is the setup as we head into next week in terms of the dollar yen sterling let's see where we're up to there in terms of data releases on Monday we get October net mortgage lending expect last time was 9.5 billion with the conclusion of the stamp duty break it's likely to partially offset the gains that we saw in September so looking for a slightly softer print there and then we head into Wednesday in the UK we get November market manufacturing PMI's last print 58.2 expecting something similar it's the final release for the month and then rounding it out on Friday in the UK we get November market services PMI 58.6 again expecting something similar final print for the month in terms of the price action with sterling we have tested into this projected ascending sorry descending trend line support and just above the s3 there and we're finding we're finding some demand come into the market I'm looking for a move back into now test 135 as as resistance from the current levels at this stage a closing breach below last week's low would would set up a deeper corrective move and we could certainly think about 131 to the downside and then into the yearly pivot at 12918 for now we're watching to see if we can get a confirmation of this reversal attempt to get us back up into that 135 zone and last but not least down under in Australia on Monday we get third quarter business inventories last time 0.2 looking for a flat reading really this month inventory rundown is probably associated with the Delta lockdowns that have been witnessed and then coming to Tuesday we get the Q3 current account balance last out 20.5 billion looking for 29.3 billion up on larger trade surplus and higher export prices are anticipated to boost that print we get Q3 net exports last time minus one looking for a plus one this time export volumes should have rebounded off a week base and import volumes of debt due to delta concerns we also have rba deputy governor guided bells speaking at panel symposium regarding indigenous economics then we're heading to Wednesday we get Q3 GDP in Australia looking at last time we saw a 0.7 percent print we're actually looking for a decrease here to minus 2.5 percent on the media market estimate again Delta lockdowns have likely impacted the GDP print but disruptions less than anticipated I think in terms of the Australian economy then we're heading to Thursday we have October housing finance last time out minus 0.14 1.4 percent looking for a positive 2 percent print this time as reopening saw a lift in turnover in October we will also get trade balance data for Australia last out 12.2 billion a little bit of a decline here probably down to 10.8 billionish imports have rebounded and export earning pullbacks are likely to continue and then that wraps out the Australian economic data for next week so let's take a look at it from a technical perspective we as anticipated in some of the uh daily technical updates that I provide for the Aussie dollar through the tick mill trading view account took out the trend line extended lower we're now testing this support zone 71 look for a bit potential for a bit of back and fill here but ultimately now I'm looking for a breakdown and we we test the yearly pivot here at 69 84 and then trend line projected descending trend line support coming in at 69 20 before we see a potential for more meaningful bounce so any pullbacks here and certainly into the uh ascending trend line support now to act as resistance in the 72 70 will be premium shorting opportunities but I anticipate that any back and fill is likely to uh extend lower now at this stage in terms of the Aussie dollar and that concludes the weekly market outlook for week commencing the 29th of November as always traders plan the trade trade the plan most importantly manage your risk until next time thanks very much


Okay. Good morning everybody. My name is Dario. I run research at the Wikimedia Foundation and I'd like to welcome you to this ground-backed talk about site agents in Wikipedia. I'm thrilled to have an extraordinary guest today, Jack Bilder Crossref, he's Director of Strategic Initiatives at Crossref. For those of you who don't know, Crossref isn't sure that Jack is going to give you plenty of context on this, but if you see that this meaningless sequence of characters is click-hung when you want to access the product paper, you can blame this guy. So we're going to talk about the interface between Wikimedia and Crossref and talk about site agents and data. We're going to have two presentations. So beautiful logistics. There's a live stream you can follow on YouTube and for live conversations you can join the IOC channel with Wikimedia and also where Abby will be moderating. So two presentations we're going to have a discussion at the end of this. And with that, I'll give you Jack Bilder. Okay, thanks, Dario. So I should probably start a little bit about explaining something that your character says that Dario mentioned and also probably a little bit about yourself. Dario mentioned I'm the Director of Strategic Initiatives at Crossref and that's really a great news way of saying I do good stuff. So I'm kind of already directed there. And a bunch of the things that have come out at Crossref, some of which you might be familiar with, some of them are things like Crosscheck, which is a plagiarism check system that seems to have a scholar publishing industry, FundRef or Fund for Identifiers, which are used heavily for trying to identify research that comes under certain funding branches. And the thing that you may be seeing a lot more of is Orchid, which actually also came out Crossref and my group, and then spun out as a separate organization. So these are the kinds of things that we work on. Largely, I think, if you were to characterize stuff that we work on, it's infrastructure, scholarly infrastructure. And ideally, one of the things that is characteristic of infrastructure is that it looks like this, right? That is that when it's working, you don't notice it. It's only when it breaks that you actually notice it. It's only when things are incompatible that you notice it. So DOIs to the degree that you don't notice them consider that success. Having said that, that leaves us with a kind of difficult situation, which is that we often have to explain a little bit about what they do because it's not immediately obvious what they do. So some background with Crossref were a 5,000 membership organization, we're a non-profit, and generally our members are scholarly publishers, but we actually define publishers with a small p. That is, it's not necessary that they be professional publishers. You've got lots of members who, for instance, have publishing operations, but don't consider themselves to be publishers. So places like the World Bank, the OECD, you know, the International Monetary Fund, like that. We don't discriminate on business models, support all contents types, all disciplines, and we also have a lot of library affiliates and things like that, joined Crossref as well. And what we do, and our major claim to fame was this DOI, DOI stands for Digital Object Identifier. It's the identifier that's digital, it's not necessarily the object that needs to be digital, that's being identified. And these things, as Darryl mentioned, are kind of weird or fake string, but they have a particular function in scholarly communication, and it's this. In the late 1990s, early 2000s, when publishers were putting content online, scholarly publishers were putting content online, they realized immediately that of course one of the most useful things that researchers could do would be to be able to click on references where the reference was available online and be taken automatically to that reference. It seems obvious, but there were some logistical problems actually doing, not the least of which is that there are thousands of publishers and you had to have some sort of idea of what the URL structure was going to be on the publisher's sites, and then of course if publishers changed that URL structure or did anything weird, a lot of those links would break. And this is actually the major problem Crossref is trying to address. And it may seem like an easy thing to do, I mean, after all, one thing that you could do is tell publishers to stop creating things, right? Update your URLs when you're modified by your websites. And that works for a class of broken links, and actually probably for the vast majority of broken links, but that only addresses one issue of what happens when things typically broke break. That is when an organization is too lazy, you know, they go and they change the structure of their website, they still control the domain, but they don't redirect to the old URLs. That's by far the most common source of broken links. But there's another source of broken links that's a lot harder to deal with, and that's when this part of the URL changes. That is when an organization renames itself or splits into two or merges or does something like that, then you might not have control of the URLs anymore, and so you need some mechanism in which that actually support things still even when these situations occur. And you might think that these situations are uncommon, and you might think that they're particularly uncommon for publishers and government organizations and kinds of entities that participate in Crossref, but there are a lot of reasons that these things can change. It's not just that things get sold, it's, you know, creation, transfer, poverty, forgetfulness, and increasingly, as you see people using URIs as personal identifiers, sometimes that means that you no longer control the domain that they control at one point. And as I say, there are a lot of reasons that links break, and if you just go and, you know, a lot of people think, oh well, university links aren't going to break, and it's been around for a long time. Government links aren't going to break. Actually, government links have highest rate of, it's almost any kind of organization, which is kind of not surprising if you think about it because if the government comes in, what they want to do is sort of obliterate the history of the old government so they rearrange all the websites and they don't really have an interest in interacting with people to think. You know, countries change name, you know, all of these things happen, and that's when the domain name might actually change. So these are the things that we're trying to address, and particularly we're trying to address them, well, one reason, of course, is because links are important to your votes, just like they are in the web, but more importantly in the case of scholarly articles, they represent the evidence record, the scholarly citation, right? These are, if this goes away and no longer see the evidence for the claims of remaining scholarly articles, and in addition to that, I might not know who is making those claims. So this is really important, particularly in the case of research, so we really had to do it. Now the mechanism that DOIs use is not, it's not a technically difficult problem to solve, it's a more socially difficult problem to solve, but what DOIs do is they give you this, they work very much like a card catalog, right? If you go to a library, a physical library, in the old days you would go, and if you looked in the card catalog, they would not tell you, the card catalog would not tell you a book was, you know, on the third floor on the fifth shelf from the back, it would give you a call number, and then the call numbers would be mapped to physical locations. And that meant that if they rearranged the library or reshelf books, they didn't have to update the card catalog, they could just change the mapping of the call numbers. And DOIs work exactly the same way, that horrible opaque string that Mario mentioned earlier is in fact the URL that is a pointer. It goes, it says, okay, where physically does this URL exist now? So if a publisher changes their website, or if they get acquired, or if they change a domain name, or something like that, all they have to do is update those pointers, and all the old links using the DOIs will continue to work. So this allows us to persist links, you know, and this is an important term, persist, we don't claim that they're going to be permanent, persist is sort of a synonym more for stubbornness, say they're stubborn links, we will, you know, do our best to update them, we will contact our members, we will do things like find them and turn off their ability to posit if they don't, they don't update things. But by and large they do, because of course this is a great source of traffic for them, it's a, and they take this stuff seriously, they take scholarly references seriously. So that's the way that the system works. As I said, technology isn't important, it's a simple redirect, almost every URL shortener on earth uses similar technology, but the thing is that they don't actually have an organization behind it, and they don't have a membership model that requires the members to actually adhere to certain conventions and to actually behave in a certain way. So the technology is very similar, but the organization is a little bit different. And again, this organization, you know, mediates this problem of having to do multiple bilateral agreements between what really are thousands of publishers, you know, a lot of people when they're thinking about scholarly publishers to the degree that they know them at all, think of the big ones Elsevier, Spur, Nature, and so on and so forth. And of course there are members, but if you actually look at our membership list, it's now approaching 6,000 members, and that's an awful lot of people trying to track them. So this central linking switchboard really helps. So what does this have to do with the wikipedia? All right, well years ago when I first joined Crossref, one of the things I was interested in doing was understanding a little bit about how non-scholarly content was making use of scholarly literature.  And at the time, one of the things that I looked at, this was about back in 2007, was I looked at wikipedia, and I downloaded a dump of wikipedia at the time and analyzed it to see how many DOIs seemed to be being referenced and also to look at references themselves to see if they might, even if they didn't have DOIs, whether they looked like they were scholarly. And at the time the answer was there were a few, but there were a lot. And so I sort of promised that I'd go back to the problem later on, because it also looked like that things were changing and things were growing. And we did come back to that, to the problem. Somebody from my group, we went and we took the referral logs for the DOI system, that is the things that show us every time somebody clicks on a DOI and where it redirects to. And we looked at those referral logs to analyze who was driving traffic to our member sites. And we were interested broadly in just understanding particularly how traffic was being driven from non-publisher sites, because we have a pretty good idea of how much the publishers themselves drive. And when we did that, we were really, really surprised because we learned that wikipedia is the fifth largest referrer of DOIs to the scholarly literature in the world.  There's a lot of noise in them, but I'm pretty confident that this is the case now after having looked at the logs again. And I'm actually, you know, I think that the potential here is actually an understatement, because the other thing that we've done, and I know Dario's done this as well, is we've looked at wikipedia articles to determine, A, how many DOIs there are in the references and how many of those DOIs, how many are linked, and not all DOIs that are in the references are linked. And then secondly, not all references to the scholarly literature use DOIs. And that's largely because they're sort of a, you know, something that you only know about if you're in the trade and you wouldn't necessarily know if you weren't a researcher that should add a DOI, and even if you were a researcher, you might not know how to look up the DOI and that you should link with it. So we think that there's probably a date or probably a lot more references to the scholarly literature, probably, and even traffic suggests. So what this translates to is that back in 2013, when we first, when we last analyzed this data, I tried to get a refresh of the data to present today, but it's still being processed, so I don't have the numbers. But back then, it was about 20,000 to 30,000 girls a day. It was increasing by about 2,000 girls a day during the eight month period that we analyzed. And the top 10 subdomains that we saw coming in were naturally the English wikipedia, but then also a lot of traffic from some of the other local language wikipedia. So this really made it clear that we wanted to engage more closely with the wikipedia and to see what we could do to A, understand the, you know, the traffic that was coming from there, and B, also see if we could actually help the editors of wikipedia articles to link persistently to the scholarly literature. And so I had run across Daniel Muchin, some of you might know a number of times, a number of conferences, and we had been talking about this for a long time, and he suggested that we put together sort of a little group of, but I think he termed it wikimedia ambassadors because they weren't wikimedians in residence quite. They were people who were interested in scholarly literature. Back in London at wikimania 2014, we first met and talked about some of the things that we might be able to do. The initial group was Daniel, Matt Sennett, Max, a million Klein, and Dorothy Howard. But then a few of them got distracted by other things, and ultimately last year we got together with Max, a million Klein and Anthony DeFranco to work out on a project specifically to take advantage of something that had just been launched by on the wikipedia, which is a live stream of edits, and to see what we could actually do with that stream. And so we worked with them and they prototyped the system that they called Qasaitis that looked at this stream and tried to pull out DOIs. And a few, I think a few months later, sometime in March, we announced that we had a live wikipedia stream of wikipedia edits, showing things like in your real time, whenever anybody cited or critically unsighted a DOI in any of the wikipedia. So if you go right now, I hope it's live, to wikipedia.labs.crossriff.org, you'll see basically this live update of wikipedia edits, and particularly articles that are citing the scholarly literature, and as I said, sometimes unsighting as well. And this has been a really useful exercise for us because it's really got our members and scholarly publishers interested in traffic that's being driven to them from outside the scholarly literature. It's really highlighted this as a source of a lot of interest. And so they've really been encouraging us to actually further explore what we can do to encourage people to link using persistent identifiers and B for us to monitor this and help build tools that help people not just use DOIs, but use other kinds of persistent identifiers like PMIDs and workables and stuff like that. And most recently, just this week, again, Joe Woss, who works for me in Oxford in the UK, decided that he was going to take advantage of the fact that the Raspberry Pi Zero had come out. And he went out and he managed to snag one of the last ones before they sold out. And at the beginning of the week, he posted this little experiment that he conducted where he put together a Raspberry Pi, a basically a real-time framed Raspberry Pi Zero that's showing us how many DOI citation events or uns citation events are occurring. And so now we proudly have this on our wall in our office at Oxford next to the refrigerator. So if that doesn't show you how important we think this stuff is, nothing, nothing. And I'll just note that the other thing, the thing that is hanging below here is a collection of telephone adapters that I put together in the old days when I used to travel a lot and I used to have to carry all of those things around with me all the time. If there's a better illustration of the benefits of standards, I don't know what it is, but that's also hanging up in our office. So ultimately, I think, again, this gets back to an overall goal that we have at Crossref, which is to understand how non-scholarly sources are engaging scholarly literature. We think that this is important for us to monitor. Increasingly, it's important for researchers to know this, for funders to know this, for publishers to know this, to know that research, the formally published research is having some sort of an effect that is being accessed by civilians out there, not just scholars. And so ultimately, our goal is to feed this into a project that we'll be launching in the middle of next year called the DOI Event Tracker, which is a general purpose framework where we want to gather information about the usage of DOIs in all sorts of different sources, whether it's Wikipedia or Twitter or Logpost or social networking sites, so that we can build a pool of data that can be used for, amongst other things, building awareness applications, perhaps building metrics and so on and so forth. And again, we see this as a thing that we're uniquely positioned to do because we can basically create a mechanism for collecting information for our 5,000 members from however many different social media platforms or other platforms that might be engaging literature. So we're building this tool that goes out and collects data and stores it and makes it open and this is really critical. As we collect this information, we want to make sure that people can maximum use it. We don't want to compete with the organizations that are doing sort of professional stuff, general category of organizations that are building alt metrics that are doing value add services, reports, analyses and things like that, but what we want to do is make sure that these things don't turn into the equivalent of the Thomson Reuters of our day. That is that Thomson Reuters is a big organization, well there are two big organizations that basically are the only organizations that have an overall view of traditional site patients in the literature and I'm sure Daria is going to talk about that in his talk, but we want to make sure that if we're collecting new sources of information about usage that that data is open, comparable, auditable and portable. That is we want to make sure that this raw data belongs to the community and that everybody can make use of it. So the next things that we're going to be working on and that we're looking at working on and part of our goal here visiting Daria is to actually see whether we can collaborate on some of these things. We're interested in further analysis of the referrals by subject category. We have a strong suspicion that the DOI referrals that we're seeing that is clicking on DOIs and following the literature occur in specific subject categories, possibly biomedical literature and stuff like that. We want to also start searching for directly linked references. Those people who for instance linked something on a publisher site didn't use the DOI and put the publisher URL to the article in there and see if you can map that back to the DOI and collect that kind of information and search for unlinked scholarly references and not just articles but monographs into what we call the sort of orthogonal related literature, things like patents and standards and other things that are sort of closely tied to the scholarly literature and often interact with them. We're very interested in working with people to improve citation tools that are being built for Wikimedia and for the Wikipedia platform. So for instance, there's a lot of interest in collecting some of this information and making sure that references are pulled out of Wikidata and we're happy to feed Wikidata so that they've got those references so that not every reference is an individual string. We think that by doing this you probably will get a lot of benefits for instance by using some of the Crossref metadata you'll be able to do things like flag references that open access literature, flag references to literature that perhaps has been updated, that is for instance corrected or in extremists retracted or withdrawn. So we think that there are all sorts of kinds of tools that can be built off of this that will make the reference and citation process in Wikipedia sort of more useful and more dynamic. As I said, strong suspect, I'll talk about some of those projects. So basically that really is what we've been doing with Wikipedia. I think it's an overall project of ours to look at how things interact with scholarly literature. Wikipedia is by far the largest source of referrals and contact with the scholarly literature that we've seen. I say it's like banks right under the top sort of aggregation platforms like SOFIS and Web of Science, things like that. If you're interested in tracking the stuff that you do further, look at our blog.crossref.org or labs.crossref.org where we post all of our experiments and talk about them and of course you can contact us directly. Joe Was is the person who's been working most directly on a lot of this kind of stuff. The person's built that tool, but also a lot of it depends on the other guy in my team, our award API that Dario and others are planning on using to mine some of these references and of course you can contact me if you're interested in any of the stuff. I'm sure I'll be working, we'll be working pretty closely with Dart. So anyway, that's it. I think I actually hit. Fantastic. Thanks, Jeff. This is the best possible introduction to my talk a bit of a lot of. Brenda, can you help me switch to the Google Docs? What we're doing is I want to say thanks that Jeff covered one issue that is really critical in terms of the disappearance of links, link rot. Huge, huge issue if you want this network of references to remain persistent and available. I'm going to cover another aspect of issues that we have with links and sourcing. This can be the main focus of my talk today. Okay, this is working. Yeah. All right. So briefly, what I am going to do, I, the regular researcher at the Wikimedia Foundation, today I'm not going to talk about what my team is doing. I'm talking about something I'm passionate about and something I've been collaborating with for a while. So I'm really interested in the question of how we use open knowledge and collaborative created reference materials like those that you find on Wikipedia as the entry point towards the scholar literature, right? And Cameron Nealon is the one who came out with his brilliant awarding of Wikipedia as the front matter of all research. He also did this great workshop at Wikimedia in 2014. A few years before the Chronicle of Hard Education called Wikipedia the highest layer without formal vetting that represents the ideal bridge between the validated and the unvalidated web. And I found this like a pretty strong metaphor for what Wikipedia represents. So typically when people start talking about access to scientific knowledge, the audience expects like a rent about paywalls, about how broken the publishing model is. And today instead I want to talk about technology.  And the reason why I want to talk about goats today is that I want to give you an example of what I like to call the disappearance of provenance. I think this is the main topic I want to talk about and ways we have to counter this general tendency that we see today on the web. So my goal today is threefold. I want to try and persuade you that algorithms used by search engines are undermining linking and sourcing. Otherwise two of the fundamental penises of the open web and ultimately Wikipedia, which builds itself on the notion of sourcing and verifiability. I want to try and persuade you that wiki data is a solution to this problem. And finally I want to introduce you to a little known project led by the community called wiki project source metadata on wiki data, which is actually trying to address this issue. So back to goats. As I'm sure you all know, Wikipedia has an amazingly well-resourced article about goats. Their anatomy, their diet, their evolutionary history, their presence in fiction and pop culture with dozens of references and up to 135 languages. We really have plenty of very well-resourced information about goats on Wikipedia. And somewhere in the middle of the English Wikipedia article, you will find a very inconspicuous sentence about the average lifespan of a goat with footnotes and references that you can look up to verify that statement. Now this is what a popular search engine gives you when you search for the average goat lifespan, 15 to 18 years period. Now let's take a look at this. This information seems to have been extracted from Wikipedia, or at least from the source that is cited by Wikipedia, but it's presented with no provenance information whatsoever. And you can try this exercise with plenty of statements that you can think of that they can have like a quick answer of this kind. And this is not an accident. This is something that search engines are heavily investing into. I don't know if many of you have heard of the Google Knowledge Vault, quick show of hands.  So the Knowledge Vault is one of the most ambitious projects Google is currently working on in terms of like going beyond the free base as its semantic engine. And the idea is that Google can currently crunches vast catalog of documents in its index and generate what they call confident facts. And confident facts are basically triples, like statements like the ones we just saw. They're extracted from all the sources and they are given a given level of confidence as a function of how many sources back the statements. So in other words, references just use a signal to determine the confidence that Google has in the truth of these statements. They're not necessarily represented as the output of the algorithm, right? They're instrumental to generating these statements. And I think this is a trouble that we need to be aware of and direction that raises some interesting questions.  This is basically the dream of the link data vision, right? Where you can have a quick answers that you can generate just by looking up a pool of connected facts. And with most people shifting to mobile, this need for quick answers is becoming even more pressing. So this is an example of what you see when you ask Siri, how long do Greyhounds live? You get an answer in these cases from Wolfram Alpha, but it's pretty much the same story, right? The answer is about 11 years, no source whatsoever. And interestingly, if you go and check what Wolfram Alpha is doing, they have a big disclaimer and they tell you that you shouldn't really expect to pinpoint any single source for that statement. They really warn you that is not what you should be doing. And the reason is the same.  They want you to trust the answer engine, right? As the authority for that state. Providence-free information, that's what users are looking for. They only care about the actual statement, not the source backing it. So David Weinberger made a fantastic and compelling point back in 2012 when he basically called out the despicable behavior of these websites that are linking to themselves, as opposed to linking to external sources. And he called these sites a stopping point in the College of Information. And he basically argued that by taking the links out of content, these websites are turning themselves into the authority for this information, right? As opposed to allowing people to verify these knowledge claims in the original context. And in a recent conversation with him, he actually reminded me that there's a war for this and there's a beautiful precedent in history for this notion of Providence-free information. And that's the Almanac, right? So the Almanac is basically the best example of an answer engine that doesn't give you any context about the source. And humanity in 2015 is recreating the Almanac thanks to algorithms, which is an amazing achievement of our civilization. So we do have a project that is an answer engine that doesn't quite work like an Almanac and that acts as a Providence-preserving answer engine. And that's Wikipedia, right? So Wikipedia is all about verifiability. It's not about truth. It's about providing access to reliable sources. And its reputation does come from the fact that you can look up and verify by yourself this information. That's what makes Wikipedia such an outstanding public service. That's why Wikipedia is not an authority in itself. It's an entry point towards authoritative sources. And you might think that we're doing a good job, given this is basically our official mission, but things are far from being perfect. What you see here is the breakdown of statements we have on Wikidata as a function of the source that they have. And up to 80% of these statements as of last month are either using Wikipedia as their main source or have no source whatsoever. So only roughly 20% of total statements in Wikidata do have sources, an external source that is transparently represented as a back in that statement. This is giant tech data on the quality of data that Wikidata is generating. So how do we start tackling this problem? And I think we have an outstanding opportunity at Wikimedia to address this issue by building a human annotated collaborative repository of all citation and source data and try and bridge the gap between information we present on our projects and the sources that back it. So in a perfect word, where all information can be represented as a connected pool of statements, metadata about the sources and sources themselves should be part of this database, right? There's no reason why this should not be part of a database. And there have been countless efforts to try and build a comprehensive repository of open bibliographic data. And Crossref represents probably the most accomplished effort in the direction. But Crossref only represents some of this data and makes only some of this data publicly available to the general public. And Crossref data, for example, is not integrated into a knowledge base. It's very hard to go from a bibliographic reference and maybe the author information to the topic of that reference or to the institutions to some extent. Basically, Crossref data is a fantastic but insular source of information about sources. How do we connect this to knowledge bases that we're so good at building here at Wikimedia? So within Wikimedia itself, there have been at least 10 years of attempts that I'm aware of at building a repository of bibliographic information for all these citations that we have in Wikipedia. These efforts go back to 2005. None succeeded. They predate largely Wikidata. But finally, we're here. So finally, we have Wikidata. And I think we have a project that has the vision, the technology, the community who is geeking out about metadata and structured data, the ability to operate at scale, the right licensing model, because we're talking about mostly CC0 non-copyrightable information. And finally, and importantly, the independence. Because we want to have a service that is not a spin-off of a publisher, but is a central repository that is not subject to commercial interest by third parties. And so basically, we have all the conditions today. Wikidata has all we need to start building this human curator repository of all citations starting from citations using Wikipedia. And these sum of all human citations could then feed into a large ecosystem of consumer services. Internal integration of Wikipedia and Commons. Jeff mentioned automatic services before that are measuring the impact of citations. And also, we have, of course, all of the providers of scholarly metadata and identifiers. So how do we start building this? Well, it turns out there's a group of people in the librarian and scholarly and Wikimedia community, including myself, who got together and started figuring out how to build this. So meet Wikiproject source metadata that you can look up on Wikidata. So the first goal of these people was to try and determine a data model for storing source metadata in Wikidata as items. The idea is, what are the properties that we need to represent the core set of information we want to store for any given citation, like journal article properties, journal properties, book properties, author-related properties and so on. And I think we did a pretty good job. We have now a solid schema that has been discussed and invented. And we start having, in Wikidata itself, plenty of publications are already stored using this schema. So this is an example of a journal article published in plus one, one of the journals by the Public Library of Science. This is the title. You can see the license, the language in which it's written, the authors, publication date, and main subject and even a picture of what this paper is about. So all of this is possible today and we already have quite a few publications in Wikidata already using this schema. But what are the actual benefits of this approach? At the end of the day you could say, you know, we have all these references in Wikipedia already and they're structured to some extent. This is what a template looks like in English Wikipedia, site journal template. Now the trouble with this approach is that right now all of these citations are buried in the body of an article. There's no way you can interact with them as items in a database, right? You need to parse content. It's a huge pain to try and make any sense of this. And so people still, if they want to say something, they need to add a reference to the actual article. How, if we could instead cite by reference, as you can do in most reference managers today, how we could pull the reference from a database like Wikidata and cite it by referring to the item that exists in Wikidata and generate the appropriate reference string. So this is possible today on many Wikis by using these templates that allow you to pull information from Wikidata. This is going to be one of the first benefits of using Wikidata as a source for this data. This is going to be a bit of a geeky technical slide, but it's one that I feel strongly about. So Wikidata can also be the place for storing all the mappings of identifiers. And if you've never heard of an identifier mapping or 3D control, you can safely skip this slide, but if you have, and you've played with something like a Magnus Mix and Match tool, for example. So this tool that allows you to cross-reference Wikidata items with external catalogs, you realize how important it is to have a place where you can represent the fact that a given scholarly article that is identified on Wikidata via its own QID is the same as an article that has a DOI, a PubMed ID, et cetera, et cetera, and the same for authors. So authority control via ORCID, via VEAF, via Google Scholar ID, and you name it. So Wikidata can become the place that holds all the mappings that describe the same objects. There's also this growing interest that Jeff mentioned in measuring the impact of citations beyond scholarly citations. So this is an example of a popular service called Outmetric.com. And what they're doing is similar to what the DOI event tracker is doing. So they're basically monitoring usage of scholarly citations in Wikipedia, mostly to give credit to authors, to funding bodies to show, hey, your work is actually being cited, not just in journals, but also in the most popular encyclopedia online that people are reading on a daily basis. So to me, possibly the most interesting implication of this project is the ability to cross-reference sources, as I said, with the vast body of knowledge that we have in Wikidata. So again, if you're using a cross-reference API, you'll be able to know something about this specific DOI and the direct metadata associated with it. By caching this data in Wikidata and cross-referring this data with all the other structured data we have in Wikidata, you can start going from a paper to its main subject, from the subject, in this case, to the taxon that is referenced in that paper, from the paper to the author, from the author to the affiliation, from the affiliation to the location, and you name it. So this is basically the rabbit hole applied to structured data. And so the next two slides are a bit of a moonshot. So this is something that's not within the scope of what we're considering is going to happen anytime soon. But once you have source metadata in Wikidata, you can also start thinking about annotating the sources themselves. And so Jeff mentioned licenses, like knowing whether a specific paper is open license or not is something that got quite a lot of attention recently. That's something the cross-reference API can provide. But you could imagine of adding other types of properties to these papers. So for example, retraction, you can start representing links between different sources, for example, A citing B. You can even go as far as representing the semantic type of these relations, right? There's a proposal for what is called citational apology that has been discussed for a couple of years. And you could imagine the given Wikidata allows you to design arbitrary relations, you could imagine properties allow you to describe that the source A extends the source B or that source A is using methods or data or whatever already used in B. Or even express disagreement and conflicts between sources. So stuff that, as of today, is really, really hard to extract just by looking at the citation graph of the literature. And finally, once we have all of this data stored in Wikidata and easily queryable and analyzable, you can start answering some of these questions, which unfortunately today are virtually impossible to answer at the push of a button. Give me all the publications in pharmacology from the 90s that have been retracted. Give me all the facts that we have on Wikidata that are backed by works of physicists who graduated the given universities in the 80s. Give me all statements that are supported by articles published in the New York Times, et cetera, et cetera. So all this work that is really fundamental for the validation of this knowledge as a function of sources will suddenly become possible if we have all this data queryable in Wikidata. So I'm going to close here and say that in some, we have the ability of start building an answer engine that is not an almanac, an answer engine that is a preserving provenance, and we can do this by using existing technology, basically Wikipedia and the properties and data models that we already have on Wikidata. The next steps, we're talking to a bunch of people to run a pilot to try and populate Wikidata or potentially sandbox version of it. James here set up an instance similar to Wikidata called library base that we can use for testing and sandbox purposes to try and see if we can get a community to import and curate this data. We're going to start designing strategies for automatically importing some of this data from, for example, the Croser API and linking them to the corresponding statements. We need to understand and we need to refine the data model if we need to represent properties. Some properties are not the correct ones. There's a big, big issue of entity disambiguation. Once you start having all these authors called John Smith with multiple, multiple IDs, the question becomes how you disambiguate them. And finally, most importantly, how can you design a system that allows you to ingest this data while preserving the human curation layer? It's probably the biggest challenge, right? It's easy to import all this data automatically. I think that 70 million DOIs you guys have will be relatively easy to ingest. It's not an impossible task to ingest into Wikidata. The question is, how do we do this in a way that preserves the human curation layer? And with that, I conclude here. Thanks for your attention and I'd be happy to take any questions. Jeff, you can join me here and we have a relaying questions for my RC. It is. Look it up on Google. Hey, Dario. Yes. This is Jake. I might have missed. Is this question time? It is. Hi, Jake. Great presentation, both of you. Awesome stuff. I'm curious, obviously the vision here is to capture all citations ever, but our kind of shorter term or more proximate issue is just capturing all the citations that are on Wikipedia. I was wondering if you could talk about the relationship between those two stages? Yeah, that's a good question. And the short answer is, I don't know how this is going to play out. What I know is that we want to start small. Like I said, we cannot just start thinking of ingesting all this data. Again, 70 million statements is not an impossible size comparing to what we could store in Wikidata. But that probably wouldn't be desirable. So I think the first step is going to be after we sandbox and test the data model, like I said, focus on one community. We have people in the chemistry and biology, you know, genetics community very interested in exploring this idea of a pilot and basically start from there. And yes, I guess the first ambitious goal is to have all citations from Wikimedia projects stored in Wikidata. And then the next step is going to be to put Scopus and Web of Science out of business. It's going to take a bit longer time. If there's not anyone else asking questions, I might. So Alex Stenson and the Wikipedia library team, one of the things that kind of brought us into those conversations looking at how libraries make recommendations to researchers doing work in their libraries and using their databases. And what we have with Wikipedia is hand curated citations that are kind of grouped by topic areas based on how categories of Wiki projects work on Wikipedia. So we can kind of tell like if a X journal shows up and, you know, certain percentage of these 10,000 articles in this topic, this might be a journal to start your research on X type of novel, right? Or X type of biography or whatever history. So I think the human curated element of this is really fascinating to me because we could take the human curated citations from Wikipedia and make really robust kind of research build into library infrastructure. So I'd like to hear you talk about a little bit more about that relationship between like making recommendations for researchers and the human bit that's so powerful Wikipedia. Jeff, any thoughts on this? I mean, see if this, so there was something that I was going to say and that is that one of the reasons that I'm actually interested also in getting a better, that I think we're interested in getting a better picture of where there are lots of scholarly references and where there aren't is because I think I have a suspicion, particularly that the references, if we're getting lots of references being followed from certain articles that there may be, that it may be the case that a lot of Wikipedia articles are serving a purpose that was one that has traditionally been performed by what are called review articles. And I've got the feeling that the Wikipedia might actually is starting to become an entry point for people who are doing research in this. And it sounds to me like that's kind of related to what you're talking about. And if you could, in fact, flag those articles and say, hey, actually, these seem to be really good entry points for certain topics and so on and so forth. And also, I think that that might be quite useful as far as recommendations go. Yeah, the other thought that I had was related to the maintenance of select quality of this data. I know in some cases we have author names that are misrepresented. And I guess one of the assets we have in Wikidata or in Wikipedia in general, is that when we cache this information, we can also revision control it. So if a human notices an incorrect spelling of an author name, or in some cases, their actual titles that are not correctly represented, or there's a mismatch between, I guess, the official record on the publisher site and Crossref, I guess one way of thinking of the human duration layer would be also to address that issue of potential errors in data. Right. And I mean, Crossref has been interested in this area a lot too, because one of the things that we find with things like Orchid and Crossref and things like Archive and even PMC is that none of them are set up so that other people can make assertions or corrections about the metadata easily. And in our view, we would like to encourage, and this is part of what we're looking at with the DOI event tracking system, is to allow people to put information in there about DOIs that maybe hasn't been put in by the publisher. So for instance, if a funder knows that a certain DOI identifying an article, that that article was about something that had been funded by them, they might want to be able to make that assertion. Or if the researcher does that, they might want to be able to make that assertion. As far as versions go, we actually think that collecting all of those statements together might be useful, even if they contradict each other. Of course, if they don't contradict each other, that gives you a bit more sense that it might be actually correct the statement. But if they do contradict each other, that's also useful information to know. So we're interested in sort of storing these assertions or claims as we state them so that people can harvest them and see claims from different parties about the same identifiers. It is actually a great point. The idea of having some of this data representing wiki data means that we also have talk pages. So you now have an entry point that is collaboratively editable, where you can have a discussion about a specific source. It's centralized, and it's open licensed. So it's a better way of saying what you would just refer to. I have a question from IRC. One is 70 millions plus probably an average of three authors. So 200 million items is a rough estimate. Affirmative? Yeah. So, yeah, when we talked about, you know, the an estimate of how many items and statements would have to be created on wiki data. If you start thinking of the item itself, the authors, the affiliation, the journal, we're talking about something around that order of magnitude. So again, it's not unthinkable given the current size of wiki data. There would be a far larger than the current size of data. So that's not something considering stage. Right. I mean, the 70 odd million, and I think it's 77 million, Crossref has 77 million DOIs assigned to research objects, the vast majority of which are articles. And so we have bibliographic and non-bibliographic information for that. The thing that in your case that you have to be considering is, of course, the number of relationships between those 77 million items. And that's going to be many hundreds of millions of relationships. And that's not to mention the relationships between those things that have formal references and the things that don't, because there's an awful lot of data and other stuff out there that hasn't traditionally been included in scholarly references that, of course, I think both of us are interested in seeing referred to more robustly. But scalability, thankfully, is not a giant shoe with media. So also the problem once we get there. So I have a little question which, in fact, deviates a little bit from the referral conversation in an auto Wikipedia. So traditionally, the DOI documents or gives numbers to publications, actual publications, scholarly publications. But as publishing moves from the traditional format into more an online publication, including actually Wikipedia comments, we still have a lot of news articles that we use on Wikipedia articles. What's the future plan for the DOI to start including more of those online publications, stabilize and eliminate the role of URLs? Right. So the DOI, I mean, the DOI is just a, this is a point that I keep making. Traditionally, the Crossref DOI has been used for identifying scholarly articles. And particularly for citing scholarly articles. And that's an important thing to note, right? Because as an identifier, it works at a different level than, for instance, maybe an identifier would if it were being used as a serial number or as a supply chain management identifier. Let me give you an example, right? The EPUB, PDF and HTML versions of an article are intellectually equivalent, right? You don't want different identifiers for them for the purposes of citation. But you might want different identifiers for them if you were, for instance, trying to measure how many, you know, units you were selling of each format or something like that. So particularly in the case of DOIs, we're using them for referring to intellectually distinct objects. And that poses some interesting problems with, and a lot of people say, well, there's a whole new category of research objects that are different because they're more dynamic, because they're changing all the time, Wikipedia being a classic example of content where it's, you know, in flux effectively, except that you still kind of need this ability to be able to refer to a particular, you know, version of it. The expectation that you have of a citation is slightly different than the expectation that most people have of a link. People are perfectly content to have a link to something and, you know, and expect that thing perhaps to change over time. But if you have a citation, you want to see what the authors saw when they cited it, maybe 50, 100 years ago, right? So you'll have some different behaviors there. So I hope that I'm getting to your question, which is that we don't see a fundamental problem with using DOIs with other kinds of research objects, things like datasets, media, highly versioned and dynamic content. And this is a place that we're evolving the DOI to right now, and we're working very closely with our sort of sister organization, Datasite, who in particular is trying to figure out how to, is adapting the system to work better with dynamic datasets and so on and so forth. But the thing we're always keeping in mind is that, of course, the goal both of Datasite and of Crossref is to preserve this notion of we're citing something and we want somebody who has cited something, whether it's a dataset or a media or an article or some new kind of communication, you know, the expectation is that they will see what the author saw or as close to what the author saw as possible. That's a really important thing to keep in mind. Does that? Do I have a question for you? How about using wiki data queries to identify unsighted or poorly cited statements like statements that aren't well cited currently in Wikipedia itself? The question is how easy it is today to identify statements that are poorly referenced or sourced in Wikipedia. I understand the question. Yeah, the answer is that it's very hard, because wiki data stores a tiny fraction of statements that we have in Wikipedia. So, wiki data stores, on the one hand, statements that just don't exist in Wikipedia, anywhere, but it also stores, I guess, a subset of statements we have in Wikipedia and it's very hard to find via wiki data possible statements we have in Wikipedia that would need sources. But in other words, we have other datasets like Wikipedia, for example, that can be used to identify potential gaps. Looking at Wikipedia, Wikipedia itself, wiki data, external knowledge bases, we can try and more easily identify gaps, maybe direct attention of this effort, where it's needed in terms of sourcing. I have another question. Can you confirm about Wikipedia being used as an entry point, at least among teachers and university folks? I know, oh, he can confirm, this person can confirm. They quickly glance at wiki and look for papers and go on to dig more related papers. Maybe that wasn't a question, sorry. It was a statement, I suppose. I mean, one of the things I'm interested in doing and that we really haven't done, but would be to see if there are sort of like particularly sort of particular articles that send an awful lot of traffic or disproportionate amount of traffic in there. We haven't done that yet. Looking at fields, we have a very rough approximation of how to categorize the journals that DOIs are in, whether they're biomedical, humanities, social sciences, things like that. And then beyond that, also knowing specifically if there are some sort of super articles that are sending a lot of references, that would be interesting. And that also reminds me that we have a project in the pipeline to segment readers as a function of how they consume Wikipedia. I'm thinking of one, some of the data we were going to get from that segmentation effort, looking at whether people are looking for a quick look up of a fact for an overview of the topic or for in-depth information on the topic might also help us identify articles that belong to different types in terms of information of who is reading them. And this probably is a good time to bring up. We're very aware, and Dario in particular has been working with us, very aware of the sort of the creepy factor of potentially invading people's privacy here. And this is actually a big concern that's developing in our, in the scholar communication space period, which is we've gone, libraries have been really, really careful about preserving the privacy of their patrons in meat space. And they haven't really kind of done the same thing in the digital space, but they're beginning to realize they haven't done that. And they're beginning to get, you know, justifiably worried about it. So one of the things that we hit when we were working on this project with Wikipedia was, of course, when you switch to HTTPS, all of a sudden all your traffic went dark, right? And so we worked very closely to try and figure out what, first of all, to make sure that our systems supported HTTPS, and then also to figure out how we could in fact still get some information without personally identifying information and so on and so forth, so that we could actually continue to see how much traffic was being driven from the Wikipedia. So this is a big, big issue. So yeah, you know, we sit there, we go, oh, we want to know all these things about like the users and stuff like that, and that actually makes people very nervous as well. So. I just wanted to, as a vocal of the IRC, say, you know, gallery there, there. Thank you very much for the presentation. All right. Thanks everybody. I think this is a wrap. See you on the Internet.