Hey guys, it's Nate, aka The Foot Accountant. Welcome back to the channel. Today, we're gonna look at my Endgame Club 198 squad and kind of just review the whole entire year of FIFA 22. What a year to remember it was. And what I want to do is just review some of that with you guys today from the Red List packluck to all the great content and the cards that I still have in my club to also the market and how I felt like we navigated the market this year on this account, how I felt like I did making coins with hitting some of my goals from FIFA 21 heading into 22 with hitting a certain transfer profit and number of coins. I was able to hit those goals and just kind of want to talk through some of that stuff and maybe even look ahead to FIFA 23 a little bit. Now, I did say this video is going to be coming a couple weeks ago when we looked at your guys' Endgame teams and that was a lot of fun. But I want to take a look at mine today and it's also just really fun to like document this for years to come as I've done this for every single year that I've played FIFA Ultimate Team and posted YouTube videos. So we're going to take a look at some of my old Endgame teams from previous years in today's video as well. So if you're excited for it, hit the thumbs up and of course, subscribe if you're new. Let's start without a do, without a do the Endgame squad. No D, just fives, no defense, just vibes. I started going 3-5-2 at the end of the year and here's the Endgame 1-9-8 squad featuring 2-99 rated pack pulls with Ronaldo and Messi. The Messi pack pull of course came a few weeks. It was actually, I think, during the shapeshift or promo or maybe like the first batch of cards that was re-released.  You'll notice right away that we've got the OG, the biggest pack pull of the entire year, 94-R9. If you guys don't know the story, we packed 94-R9, the second week of division rivals rewards Untradable in FIFA 22. Literally I've had this card all year long. It's crazy that this happened. I still cannot believe it. It'll go down as my best pack pull all time. It's going to be hard to top this because he was literally 11.5-12 million coins when I packed his card Untradable. A thousand games played. I've never hit a thousand games played on a card before but I had to do it with this card because he was just fantastic and phenomenal all year long. Towards the end of the year, his goals to game ratio really started to fall off. But that was because of all the objective, preseason cup, gold and gold type stuff that we were playing. So that doesn't help. But definitely when I think of FIFA 22, this card is going to be one of the first ones that comes to mind. And this team at the end of the year, I mean, look at the cards that we have here, man. Not all end game or not all players that I would consider club legends. Really, like these are all pretty recent cards. I've had Yashin for a while. Besides R9, most of these cards have come in the past, maybe month and a half or so, but a 198 squad with so many great cards on the bench from recent transfer cards to the showdown Ericsson. It was lovely. To even I include a Kuti Armando in here because I use it in my team a lot. And so that's the end game squad right there. But what I really want to cover and talk about some more are some of the legends. And I made a squad here called the club legends just to look through and remember with me like some of these big pack pulls and how my team looked throughout the year. Now, of course, R9 up top of this squad. Second most memorable player to me in FIFA 22, 100% would be this Lionel Messi team of the year. I still cannot. I mean, this one almost meant more to me than packing R9. Big, messy guy in real life love how he plays. And this card, especially in FIFA was just unfreaking believable. I packed this card like it was literally 15 minutes before a team of these years were going to go out of packs. Got him in an upgrade pack. It was a true buzzer beater. It was a phenomenal time. And the messy finesses are something that I will never forget about FIFA 22 long range finish shots with this guy. We're just unbelievable and broken. And it was honestly kind of sad when I packed the shapeshifters and had to get rid of the messy card, which of course this is the flex, right? When you look in the club and you see the messies, we packed the team of the season. Didn't even play a game with it. Packed the team of the year and of course packed the shapeshifter. So we have 97, 98 and 99 messy all in the club. Ridiculous. I know that just goes to again prove the red list pack look year that it was. But that card really meant a lot to me. This year is a lot of fun to use. He was in every single squad that I had up until I packed the shapeshifter. You know, another card that was a beast for me this year, team of the Ercante.  I think it was and I got Kimmich in 21. That's that's what it was. But this guy is always a workhorse. He was again for me this year. Best midfielder in the game basically until the end game after tots. I still think he was the best midfielder in the game, such a good card. 600 games played plus then some other club legends that are in here. Three big team of the years this year that ran my team for a long time was the Hikimi, the Conte and the Messi. Flashback Benzema, of course, has to be mentioned. What a great value. SBC that was at the time. What a great card. Flashback Neymar is up there. Flashback Veran probably in terms of game played would be up there. 588 games played for him on my account. Crazy card. You guys might remember some of these guys from being in my team for the longest time like play the month in Bape. I used to run a lot of Serie A on the left side. I mean, if you take a look, I ran foot fantasy Alexandra for 281 games. Before that, I ran the red inform 87 Taylor Hernandez for 270 games. I think I did all versions of the foot cat or the there was a foot captains Kealini, I think, but I did all three SBCs of Kealini. I have the flashback still. I turned in the captain somewhere along the line and I have the end of an era, but this one was the one that I used the most in the mid to early game stages. He was fantastic after that card was released. I used Darmion for a while. He was, I would consider him a club legend for me and his Dean of Talley. This guy kind of brought the team all together 300 games. He had some great shooting stats as well. Of course, you can't think about this year without 87 Vakir 387 games played for me as well. It was kind of funny. I was looking at my squad earlier on this year in FIFA 22 and looking at the kind of players that I was using. This video was November 14th. So, you know, the game has been out for a month and a half by now and I'm still using rule breakers Filani, the two icons that I packed in the early game of FIFA 22, Raul and R9.  I was really stingy in FIFA 22 and that's something I think one of the things I want to get better at in FIFA 23 is not being as stingy. I mean, I'm still going to be very careful with coins and I think that it benefits you in FIFA to be very careful with your coins. But I also want to have more fun. That's one of my first goals for FIFA 23 is just to have a little bit more fun and do more player SBCs, do more other SBCs and just be able to card collect a little bit more because I did Bernardo Silva. I did Talishka. I did Fakir, right? The Filani objective that was during rule breakers I still have that card in the club. But, you know, I also skipped a lot of stuff even through the middle part of the year and I had plenty of coins to do it. It was just more of me being like, okay, I'm going to be really strict about saving my coins. So I think that one thing I would change for 23 is be a little bit less stingy, as I said, in terms of that stuff. But last few cards in here that are kind of club legends for me. This Desai, I used him a lot, but also this marks a very, you know, a very crucial time in the year of FIFA 22. This was from the mid or prime icon player pick SBC that was giving out the moments cards. I did the SBC on my phone in a flash, packed Desai moments. So I got a compensation pick. Didn't get anything good from that pick, but that was a crazy time when we were hoping for all that compensation from the mid or prime. And then, of course, a couple other cards in here. Casa was an early game beast or, you know, December type beast for me. Veghorst is a FIFA 22 legend for everybody. What a card this was. He'll go down in history for sure. It's one of the most overpowered strikers in the game, just in a weird way. The heading stats that he had, he's nuts still to this day at the end of FIFA 22, very good. And then probably, arguably, in my opinion, the craziest FIFA ultimate team mistake and happening of all time when all of the gold Ericsson cards just turned into the foot fantasy and then this card got upgraded. I'll never forget that. I'm so glad I had one in the club. That was fantastic. And this card was pretty good. And of course, his showdown card in the end game is really, really cracked as well. So speaking of a couple other cards here in the club as club legends, you can see, I mean, the pack look this year has been ridiculous. All the cards that I showed you in that first main team, if I forgot to mention already, were untradable. Of course, some of them were SBCs, but like all the stuff that we have in here, Marcelo packed first owner untradable this year, Benzema, we packed the Ronaldo during one of the batches of footies. I believe it was, you know, a lot of these cards I haven't even used, like Salah has 10 games on him and stuff like that, but the Sun I've barely used, Dina Talley, I haven't even used this version. You know, some of the footies cards, I packed Dona Roma during team of the year. You know, Mane, actually, I did not do this card enough justice. I packed him during Premier League team of the season and untradable. This is the card that I packed and I only used them 28 games. Like that's bad, honestly, for me. Not using that card as much as I should have because that was a really, really good card. But in yet I packed on the first day of League on Tots, this was a pretty crazy card. Used him for a while. Paqueta was another one, I think of a League on Tots card that did a lot for me this year. It was in my team for a long time. Play of the month Ronaldo didn't really do much for me. Delhi Ali during Prem Tots, I still think this is one of the better value Prem Tots SBCs. And one of the better Prem team this season years we've ever had. Rodrigo was in there. There's the Paqueta card. Really liked this Wood center back for a while. He was a solid card. And then you've got a lot of fodder in here too from, you know, all the packs that are opened up right now. But the Red Davies I used a decent amount. The Flashback Harry Kane. I know I have the Tots Harry Kane in my kind of club legends side but this Flashback Harry Kane card was great as well. Love both of those. Of course, being a spurs fan that helps out there. Bunch of other kind of just random cards that are in the club still. Foot birthday Sergio Ramos was a nice one. The Phil Foden Tots card was a solid one. Kudusevsky, good amount of games played on hand. Amine Guri was a really good card as well. I barely used him but he was pretty good. You know, some other cards in here. I kept the Maxi Morales around. Shortest player in FIFA, five foot two. Haven't really used him for too many games but that was a good card. The Gavardiola was a really good card. Cali Aguri, right? This is a card to remember. 71 games played. Really insane card during foot fantasy. That Mekaterian foot birthday was great. First owner, packed, untradable Mbappe.  No other versions have I packed of him. Tried to get a Tots card to the team of the year in the end game. Wasn't able to do it. Braith White Showdown. LaCelso I used a bit. Griezmann, you know, I still kept the Ozil. I have the Defoe. I think I have the Akinfenna. Well, there he is as well. I used this Weston McKenny Showdown for a while. Still have him. Still have the Domins and Sanchez. Quarais Madest. Pulisic. You know, this was a weird one. I took the Ice Pulisic for the skills instead of the weak foot, which a lot of people did. But that was a fun card. The Ryan Kent just didn't live up to the Ryan Kent hype this year, as he did in previous years. Awar was an early game legend as well. You know, I did the Fellini. Here's the Fellini that was in my early game team. 114 games played. So I don't have any of the gold cards, of course, that I used. But I mean, there might even be a few silvers in here that were in that silver promo. But that's besides the point. What a club, right? And incredible, incredible pack pulls this year. A lot of red list luck. A lot of really good SBCs that we have done along the way to get a really, really crack team. And a fun year. But seriously, for me, it's this Ronaldo and it is this Messi to top it off. For me, it's the top two players that mean the most and also that did the most for my ultimate team this year. Now, let's talk trading. Let's talk, you know, let's talk record first. 585 games. I just broke the thousand game barrier. But I think this doesn't count. I think if I add this up correctly, it's like a thousand and seven or a thousand and five games. And my R9 has a thousand and 28. So that really makes no sense. I think there's something off there. But, you know, that's a normal year of FIFA for me. I think that just does not count some friendlies or some of the golden gold modes or something. But, you know, hiding the record this year was pretty interesting. It used to be, of course, on the top by your coins. I think that's staying the same for FIFA 23. That was really interesting. And then leaderboards. I want to talk about this too. It kind of, you know, turn it into a market kind of conversation. My transfer profit right now is 110 million coins. That's actually down a lot because I wasted a lot of coins and burned a lot of coins in the past. Basically, two months, I stopped trading after team of the season was over. I stopped for real. Maybe one or two times from here and there I kept going a little bit. But I peaked at 120. I think right around 120 million transfer profit, which going into this year of FIFA 22, I was really hoping to hit that 100 mil mark. And I absolutely went out and did that. That was a GG. Started pretty good from the year. And then after team of the year really turned it on, really cranked it up. I think the most coins I ever had total liquid was about 24 to 25 million coins during team of the season. I think it was right at the beginning of TOTS before we started spending a lot of coins on packs and stuff and like that. It was a good year of trading. It really really was. I felt like I had plenty of coins to do, whatever SBCs I really wanted to do. Maybe not so much earlier in the game, but in the second half of the year felt like we did very, very good trading wise. Now, just to talk about the market a little bit more, and then we'll look at some older end game teams here in a second. The market for me this year was a lot of fun. It was a blast. Starting from the beginning of the year where I traded with this guy.  Daniel Carrico. This guy has a non-rare gold who is expensive right now like 1.8K.  And just because he's a Portuguese center back, watch out for that in FIFA 23. You know, these guys just fluctuate so much in the beginning of the game and in the early game time period that he was such a great card to trade with and amazing, amazing coin maker for me. I mean, I remember going, I think I even went unassigned one time on buying a bunch of these at like 800, 900 coins and then selling them all at 3K, making a couple hundred thousand coins a day on this guy in the early game for a couple days in a row. Like that was a really big early game profit maker for me. And then I actually have some tweets pulled up of some trades that I did during the year. Usually I tweet every once in a while that I'm trading just to kind of post a little bit of a summary. Some of my biggest moves this year, this is the one that I remember the most. It was a really, really short flip. Like, I mean, I think the time on my phone here is like 11, 20 a.m. And I sold a lot of these Lucas Morris from between like 900 and 950K. I bought 16 of them, a flip kind of in the morning time.  Of course, I had a lot of coins. I had 14 mil at least when I was making these moves. But I think on this morning I made literally a million coins in I think it was only like an hour. Like the flip was so fast. And that was the biggest thing. Like if I think about how I made my coins the most this year, it was absolutely from panic selling and then selling on the rebound, right? There was so much panic selling in FIFA 22. And I think that that's something that I'm glad that I got good at or that I really started to get good at during the year. Not always is it perfect because when panic happens and panic hits is just sell, sell, sell. And it's really hard to time that low point and to try to figure that out. But I think I really learned this year to be a lot better at timing those drops and making profit off of those drops. Now you see here like I bought Swires for 1.187 sold them at like 135. So a lot of stuff like that where it was quick flips over a couple hours time period or especially with rewards like Monday mornings were really good days. And even if I think back to like rivals rewards the market rose really nicely on rivals reward days as well. Vlahovic bought for 5.19 sold for 5.69, 5.70 So a lot of just those really small not small but those really quick flips and a small timeframe. That's where I feel like I made a lot of my coins this year and I think that's going to be pretty consistent with me and FIFA 23 as well. I think the market's going to be pretty similar. My biggest flip ever in terms of kind of the most risky one. I bought a Pushkas. I believe I bought it right before a stream. I had 7 million coins and I bought this guy. I went absolutely broke at 35K left bought him for 77 mil. He was brand new right on the web. I didn't even have the the icon moments names in there yet and I sold them later that stream for 8.4 early on. You can see I was trading with some of the moments cards.  I made a lot of coins on icons. I was just consistently flipping them flipping them. I feel like for whatever reason I think it just had to do with FIFA 22. The icons were not that OP. So only around release dates when like the primes came out or when the moments came out or the early game was I really interested in a lot of icons and trading with them just because they weren't that hype. And you know I think that's why there's a bit of a step down of that from FIFA 21 to 22 but still made some nice flips on icons during the year for sure. That was a nice money maker and then I specifically remember some of these I was on like vacation I think at the time I bought some Hollins at like 9.45 and then sold them for 1.06 like a couple hours later like that's the sort of thing that I was doing a lot of trading with in the early stages was the quick flip. So that's probably going to be where I make a lot of my coins and FIFA 23 as well is just finding the quick flips finding the promo cards that move a lot and flipping them in a short time span. Right. And you know we think about road to the knockout cards as really good cards to buy when there's panic selling and then sell when you know the panic selling stops and stuff like that. That's absolutely still going to be a thing this year but I think with the content being as consistent or even more consistent in FIFA 23 as it was in 22 I really just think that that's going to kind of bring more opportunities to trade in that same way with the quick flips and that's definitely my favorite way to trade. I think it's some of the less risky ways to trade as well because you're not holding onto cards for very long and especially the way they do promos now and cards prices now if you're holding onto cards for super long time you're going to end up losing a lot of coins just because that's how this game is designed these days. So you know I think I'm going to make a lot of coins off that same kind of method in FIFA 23 at least hope so. The one thing I want to get better at this year though is fodder. I really do and I want to get better at watching the fodder market and being in tune with it at all times but also being able to like look up a fodder card like Tony Cruz I'm sure this is gold card impacts right now it might not be but you know just being more in tune with SBC fodder because after the first month of the game fodder is the best way to make coins. You know I'll just say it is a blanket statement SBC fodder is the best way to make coins in FIFA that is the number one way to make coins most consistent and less risk all you have to do is wait right time is on your side there are specific times of the year where fodder is almost always getting expensive. I think of like November and March as two times in the past couple of years where fodder has absolutely peaked and it's a good you know thing to remember for FIFA 23 but you know fodder makes a weekly fluctuations and it's it's so easy to trade with because there's always content that's getting released right think about you know player SBCs and especially when we have leaks and stuff like that so doing doing more of like the club stock and doing more of that kind of stuff this year it's going to be more important for me now I'm not sitting here and telling you that I plan to go unassigned a lot this year because that is exactly not the case I don't plan to buy hundreds of David Silvers or Harry Maguire's and go unassigned for weeks on end and you know making a lot of coins which that can be very profitable and if if you want to do that like this year it's going to be a great year to do that in FIFA 23 for sure as it was in 22 but again as I'm saying just for me I really think that I'm just going to pay attention to more to that market this year in form investing was really popular in FIFA 22 even with in forms being supplied more than ever because if we can leave rewards so we'll kind of get into the nitty gritty of that stuff of course as we get closer and closer to FIFA 22 but I just wanted to make this video today to kind of flex to Endgame Club and the Endgame team a little bit just the last thing really quick I didn't mention this before when we were looking at the team but this is absolutely the best the best endgame team that I have ever had just taking a look back at my FIFA 21 endgame squad that's just a testament to the pack look and the content this year right this is my FIFA 21 endgame team had the raster and then that was probably my best pack pool and FIFA 21 you know a couple nice sbc's like the 99 messy transfer the 98 Ramos the 98 Acunha that everybody loved right really really good card really good side but then if you look back at some of my older FIFA squads well first here's my transfer profit so big upgrade from FIFA 21 56 mil transfer profit FIFA 21 120 mil this year so that's the full time effect right there for you but then if we go back and take a look at my FIFA 20 squad I had 98 Mbappe 98 Neymar and 97 Bagnetta that I packed during League 1 Tots still had that 98 7 Conte that was in there some summer heat cards you see this Lahtan the Erics in so I think that this would have been team closest to an endgame squad that I have now but definitely my squad in FIFA 22 blows that out of the water and then my FIFA 19 squad that I bought not packed I think I bought that Ronaldo as well I bought a lot more cards in FIFA 19 but those are my endgame squads from those previous years and then Oh that was my early game squad from that year too so you know it's just it's just a lot of fun to look back on where you have gone this FIFA and just kind of remember the year I know we've been doing that a couple videos now on the channel but I just want to make this fun one today to look at my club is as you of course you guys have kind of come along the ride with me and that's kind of the interesting thing about posting videos right is I don't always update you on how my squad looks and how my team looks but a lot of you guys watch the stream so you know what packs we're doing you know a pack pulls that we get and stuff like that so it's kind of just fun to look back on the progression of the year to what we have had and what we've been able to achieve and what we've been able to get really good year for the spurs on tradable team as well just to flex that off at the very end some really nice cards here in the past two weeks that were charles and transfer the pairs of transfer so really good year for the spurs team I mean that the same can be said for united for city for Chelsea for Liverpool so many special cards this year I think it's just going to continue in fever 23 with the great content and more cards than ever so stay tuned for more fever 23 videos it's going to get serious real quick looking ahead into fever 23 but if you enjoyed today's video and you've enjoyed the year fever 22 hit the thumbs up comment down below if you have any questions and subscribe if you're new it's been Nathan put account and catch you guys later peace out


Welcome to All Hands Update. Here's what's happening around your Navy. The Navy encourages personnel and their families to dispose of unused and expired prescription medication during National Take Back Day, October 22. The event allows people to take their unwanted prescription drugs to a local drop-off site anonymously and free of charge. During the last Take Back Day, Americans turned in over 447 tons of prescription medication to almost 5,400 sites operated by the Drug Enforcement Agency. For more information on National Take Back Day, visit their website. A new Navadmin was released changing the way the Navy tracks limited-duty sailors and Marines. The limited-duty sailor and Marine readiness tracker system is replacing the medical evaluation board system and the tracking of temporary limited duty. To see more about the new changes, check out the Navadmin. You can read more about what's happening around your Navy at navy.mil.


Well, thank you very much for the very kind introduction. I'm not sure I lead up to the expectations after that introduction. I'll do my best. So today's talk is on data-driven materials discovery, and I'll begin by explaining the challenge of the problem and then show you how we're trying to solve that problem, giving you 4K studies from the energy sector, and then I'll finish with where we're going next. So here is a picture of Alexander Fleming, very famously discovering penicillin by chance. Now, we've come a little bit further than that over the time, but still actually we're discovering materials using trial and error in the main. And this picture here in the middle is taken from a biomedical website who are likened materials discovery to firing scalpers at a dartboard which has a moving target. Now, wouldn't it be better if there was a systematic way to discover new materials? And of course, I feel that there's an opportunity to do so using the recent advances in big data. So if you're going to do data-driven materials discovery, of course you need data. So let's think what would be the ideal source of data for data-driven materials discovery. Well, ideally you would want the entire universe of all possible chemicals, molecules that could ever exist, and for each of those chemicals, know what their cognate material properties would be. And that's because there's an inherent relationship between the structure, the molecular structure, and the function of a material. And people over the years have used empirical means to work out what these structure-function relationships are. And yet if we could encode those relationships, those patterns, about chemical and property space in a way that a computer could understand, then we could encode them and link them up to a search engine that we could then use to probe this chemical and property space to find patterns in the data that then could lead to predictions and ultimately experimental validation of new materials for a given application to suit your needs. So that's the dream. We don't have the whole universe of all chemicals and all of their properties. So what do we do? Well, to a first-order approximation, we do have that information, albeit in a highly fragmented form. And that form is the scientific literature. That could be academic papers, could be patterns, company reports, and so on. But there'll be somebody in the... lots of people in this audience who have written a paper on one material and its properties. There'll be another person in the audience who's written a paper on another material and its cognate properties. But if we could grab all of the information from all of the documents that ever existed and put it all together, then the sum is greater than its parts, we could then have to a first-order approximation all of chemical and property space so that we could predict new materials from all of that historical data. So that's the essence of my talk. And what we've done to do that is written a whole load of software that mines text the chemical and property information and extracts it, and also image information, chemical schematic information, and chemical reaction information. Here are four software tools, our primary ones. And having grabbed all of that information from the input documents that you give it, it also automatically compiles it into databases for you. So now you have a way of materials, databases that you can make for your own needs, i.e. bespoke databases, for your given application. And once you've got that compiled data, we can then essentially drive that into a design-to-device pipeline then using the data to predict new materials, using the machine learning that you see a lot now where they classify and optimize in data analytics, and then that will find patterns in the data that leads to your prediction of a new material, and then you can use that prediction and go forward with lead materials for the experimental validation. So that's the pipeline. And I'm going to focus just for time on chem data extractor, the text mining tool, just so you know. So given that, let's just have a look a bit further in how chem data extractor works. That's the text mining tool. So we've got input of scientific literature. So it can literally be thousands and thousands or even millions of documents if you had them. And chem data extractor will ask of the documents. It will always find, as far as it can, the chemical molecule, wherever it writes the chemical name, or a picture. And it will then also grab the paired quantities of properties of which you've asked, right? You ask the properties you need for your particular device, whatever it is you want to make. And it will then grab that paired information and put it into a chemical database for you. How does it work under the hood? Well, this is best explained by example. It's actually chemistry-aware natural language processing for those in the field. And what it does is it takes every sentence from every document of all those thousands of documents and it does this. So figure two shows the UV vis absorption spectra of 3A, bracket red, and 3B, bracket blue, and acetonitrum, a fairly typical sentence in science. And what it does is it takes the sentence and splits it up into its constituent parts, the words, the numbers, the punctuation, and so on. It then assigns a grammar to each of those parts. So figure is a noun. Two is a cardinal digit, or decimal. Spectra is a noun, but it's plural. And acetonitrol is what they call a chemical mention. So this is the chemistry-aware logic coming in. So acetonitrol is probably the solvent in this. You can probably read that as a human, but, of course, the computer has to think harder and use logic to work that out. Having assigned a grammar to that sentence, it then turns that sentence into a hierarchical tree. So it's a figure, and it's figure number two in that paper. It's a figure about a spectrum. And the type of spectrum is the UV vis absorption spectrum, not, for example, a UV vis emission spectrum. And it's a spectrum of something called 3A and something called 3B in acetonitrum. Now, very crucially here, you have to look at 3A and 3B because we don't know what 3A and 3B are yet, right? So if you put that all out into a database at that point, you would lose all of the chemical-named information and it would be a totally useless database. So what you have to do before you leave the document is resolve what the labels mean. And usually in a scientific document, if you track back in the paper, which the computer then does, it will usually find typically the first instance of 3A and you'll see just before it a long chemical name, right? So bracket 3A, or maybe 3A will be in bold or something like that. So if you train the computer to search for that, then you can resolve what 3A and 3B are and then put that in and then you can pull out the right chemical information and its cognate property information. So that's why it's chemistry-aware natural language processing. So there's this technology here and then we're using, let's say, standard machine learning these days for data analytics. The other actually pretty hard thing is when you get to the experimental validation. And I'm not going to talk so much about the experimental work today, but I do want to give you an overview of it because it often involves complex facilities and so I thought I'd give you a glimpse of the sites of facilities that we do experiments at. And so what we're going to do now is we're going to want to fly by of the UK's Neutrona Muon facility. There's a glimpse of the experimental world that mostly I won't be able to talk about for time reasons, but hopefully that gives you some idea. So we've talked now about the challenge, we've talked about the technology, so let's see how we apply it. And I'm going to show you now four case studies, all taken from the energy sector to see if we can discover new materials. I'm going to start with solar, the sun. Here's a picture of the sun in Cambodia, just for your amusement. And I want to discuss the idea of how we can apply chem data extractor to discover new light absorbing materials for photovoltaics. So irrespective of the type of photovoltaic device, all of them need some form of light absorption. So I want to think about the underpinning problem at the molecular level to do that. So let's look at this graph. So the black jaggedy edge here, that's the solar emission spectrum. So there would be your visible light as a function of wavelength. So that's what you see as a spectrum from the sun. What you want in terms of grabbing all of the photons under the area of that curve, that's your goal, is you need then light absorbing material that ideally would be something like that green line that pretty much grabs all of the area under that curve. So all of the photons. Now, there's no one material that really does that. So that's a problem, but people in the device technology often combine, say, two different types of molecules, say one that absorbs in the blue and then one that maybe absorbs in the red. And as a convolution of those two peaks, you will get more or less the green. That's the concept. So people often do that in the device world. So let's set ourselves the problem then of making a database using ChemData extractor that finds the underpinning molecular property information, say the wavelength maximum. So see where the peaks are of these. And we're also going to take, if it's present, the absorption information, the intensity here, which is going to be called the extinction coefficient. And we're also going to, of course, take the material, the chemical name as well. So that's the database we're going to build, material property 1, property 2. So we build that with ChemData extractor and that at the time was just under 10,000 chemical molecules and their corresponding properties. So we're going to build, grab from the scientific literature. And our goal is to get from 10,000 possible light absorbing materials all the way down to, say, five lead candidates that we can take forward for experimental validation. And the way you do that is you apply this design-to-device pipeline, like so, and we have to ask really carefully chosen questions that sequentially filter out 10,000 all the way down to just a few. So the first question we ask, for example, is remove all things containing metals. We wanted organic materials for environmental regulations. That actually made our case quite hard. Nonetheless, that's what we did. And so that went down to just 3,000. That's already a big jump. You want quite big jumps early on, so you don't end up asking far too many questions. Then we ask a question that's relevant to a device. I have a photovoltaic technology. We knew that, in the device, we wanted molecules that contained a carboxylic acid group. And that's because we knew that the interface between the light absorber and the semiconductor that makes the working electrode as a composite was working particularly well when we had carboxylic acid groups in our light absorber. So we fixed that filter, and then you see an order of magnitude reduction from 3,000 to 300, more or less. And then we go in and we say we wrote a mathematical algorithm that finds the optimum combination of those possible 300s you've got left to find, if you like, the kind of best combination of blue absorbing and red absorbing, or at least extremes. And then you go down to another order of magnitude to about 30 possible light absorbing materials. And at that point, 30, you can actually go in manually and do things. It's a manageable number. And actually what we did then is we performed some electronic structure calculations, a computation on all of those 33 to check what we call the energetic alignment of our light absorbing materials within the device. So this is the idea that we can predict all these new light absorbing molecules, but that's all in isolation. We've got to think about the device. You've got the electrolyte, you've got the other electrode, we've got to check that the energetics line up so that you get the right driving voltages. So we did that, and that brings it down to essentially a handful left. In our case, five lead candidates to go forward for experimental validation. And here are the five. And one good thing about the chem data extractor type of technology is because we've mined the data from the scientific literature, the people who originally made these materials, we can contact, right? Because we can track back, we always keep the DOI as we go through, so we can track back and find the author for correspondence. And all of these molecules, by the way, were made for purely scientific curiosity, synthetic curiosity. So none of them had any idea that may be applied for photovoltaics. And I wrote to them, I emailed them actually, said, hey, you know, we think your modern material might be useful for photovoltaic application. Do you still have some, or could you remake it, send it to us, and we'll put it into a device and test it for you, and we'll do this as a collaboration so everybody wins. And they all said yes. They all sent materials, and we put them into devices, and this is what we got. And this is a graph of voltage versus current density. For the experts in the room, remember, this is our lab work, but it's the relative differences that matter. And the black curve is an important reference. So the black curve actually corresponds to the industry standard. Now, the industry standard actually is a metal-organic material. We said we wanted only organic materials, but as an industry standard, we'll reference it to the metal-organic one. That's one of the best performing ones you can get. So if you can get your curve and your testing to get anywhere close to that black line, you're doing well. So I'm going to focus on X, X6 and 15. They were the original labels and the original publications. And if you test them just on their own in a device, you'll get the red and the blue curve for those two. So voltage is okay, but the current density is not so great, because you're not close to this black, for example. But of course the logic was to put those two together, to get blue absorbing, red absorbing. And so when you put them together, you get the mauve line, which actually is pretty close to the industrial standard, but we've only got organics in our system. So we were pretty happy about that. Actually it gets 92% of power of the industry standard. Other people were happy. It got onto the front cover of Advanced Energy Materials Journal. And so there's an example of data-driven materials discovery and action. And before I leave the solar topic, I just want to touch on one other thing, which isn't discovery per se, but I think it's relevant, that you can also use Chem Data Extractor, the text mining tool to help with manufacturing. And so because what you can also do is mine existing things that people know about. So why would you do that? For example, you can make nice histograms. So it is of open-circuit voltage, the current density, the power conversion efficiency, all the known materials for two types of photovoltaics, perovskite solar cells and Dyson solar cells. There's more on this one because it's much more mature technology, nearly 200k of data on perovskite solar cells. So that's what's known. This isn't discovery at this point, it's not just the device properties you can predict on the molecular properties, but you can also grab information about the actual manufacturing process. That might be quite important to you to know what type of solar simulator is used, the testing device, or the active area of the test sample that you use for the solar cell. That type of information can be very valuable if you want to optimise the manufacturing process. So there are side uses of chem data extractor as well. So that's the first case study. I now want to move to the second case study and we're going to look at heat. This is a picture of a volcano erupting in Guatemala. I took the photo from a safe point here. You're laughing, but wait till you see what happens next. When it erupted, I was actually there on the ridge. This was a video idea. You don't want to be ever closer than that, by the way. Let's just ready to wake you up and show you some heat. Anyway, so I'm not going to do volcanic applications, but I am going to do thermoelectrics, and this also gives me an excuse to play with Lego, and I made a little thermoelectric device, which I'll pass around in a second, but just to explain then how it works for those who are not in the domain area. What you have now here is a case where you have a cold surface on top and a hot surface under the bottom, bottom top, and therefore you have a thermal gradient, and I'm going to super simplify this, sorry, all physicists in the room. So just like in the atmosphere, if you have a thermal gradient, you have convection currents that cause all the weather, and here you have electronics essentially creating a convection current, in this case with a thermocouple, the P and N semiconductors, and so you can create sort of electrical currents and therefore voltage is driven between because you've got the thermal gradient that stimulates it from the top. So then that creates, because the thermocouples are all in series, that creates an electrical circuit, electric. So I'll just take these around. I've got two because I've already got particularly happy with playing Lego.  I promised that I'd send it back. Cool. So that's how a thermal electric material works. And in equations, here is the figure of merit, so that's ZT, that's just to understand it as your proxy for that, you know, a high ZT means a high-performing thermal electric material.  And that's because you want to keep that thermal gradient without interfering with the electrics. You want the temperature, high, right? And so if we can then use chem data extractor to mine all of these properties plus something called the power factor, then we get this distribution, right? We've literally just published this I think eight days ago. It's a new database, and just of our interest, you see all these spikes, you're probably wondering what they are, right? These are really experimental data. It's not an artifact. It's actually really real, and what they are is actually rounding up of people's results, right? People will quote things to say one decimal place, right? So then you get things spiking up to 1.3, 1.4, etc. Right? And of course we present the data faithfully. We could, of course, average it and put sort of gaussians over each of those spikes because probably they mean it was a bit more and a bit less than 1.0, right? But this is real data, right? So that's why that you see spikes. And you can do things like look at the highest ZT material, right? People may know that anyway, well, okay, but what else can you do with this? You can forecast with it. So you sorry. You could take the average, the year of publication. Remember they come from academic papers and you can check their average ZT, the figure of merit for, and then you can say, well, roughly, if that was a linear trend, by 2052 the average ZT that would be reported in papers would be 1.5, for example, right? And if you're thinking, well, should I work on thermoelectrics? Well, if this is the year of publication and the number of records that have been occurring is going up like this, it's probably quite a good field to go into right now, right? So it's good also to help you with forecasting is the point. So that's heat. Now let's look at hydrogen. There's been a lot of media interest, of course, that people have seen with hydrogen fuel cells, for example, as a technology. And therefore you need a way of producing hydrogen, right? And people mostly produce it using water. And they do something called water splitting which unsurprisingly means you split water into its constituent part, hydrogen and oxygen, right? It's good because you generate what you need, the power, the supply of hydrogen. Whoops, sorry, going back. But you also generate a clean side product, oxygen, which is also helpful. So no problem there. And there'll be a catalyst that may help. So this technique is therefore called water splitting. This is a picture of me water splitting for no reason. And here is the question, how do we apply chem data extractor to help? So, well, there's no point in mining for water, or something like that. But we could mine, for example, for all catalysts that had ever been produced for water splitting and unsurprisingly we are making a database or we've made one, haven't published yet of all catalysts for water splitting. So that may help thinking about optimising things, finding the right catalyst for the hydrogen process of water splitting. But there's another use that we could work with which is thinking about the hydrogen. Once you've made hydrogen, it's a gas, right? So we've got to find a way of storing it. Now, what you would really want, actually, is to condense it into a liquid form so that you could store it in a nice, small, contained little vessel. Now, there's not really a good way of storing hydrogen in the liquid form. Partly because the liquefaction temperature for hydrogen is about 21-ish Kelvin. That's minus 253 in a bit. Right? Degree centigrade. So that's really cold, right? So how do we find a refrigerant that will cool the hydrogen down, put it in its liquid phase and keep it cool? And we're going to say, can we find a material like that? And what we're going to use for this purpose is we're going to try and discover a material that's a magnetic refrigerant. So this is the idea that so some materials will be heated up when you apply a magnetic field to them. And when you take the field away, it will cool down, right? So that can be a way to potentially make a sort of, this is just my avatar, if you like, of magnetic refrigerants. I'd not really like a fridge, but to give you an idea. So what are the properties that make a magnetocaloric effect material? They're these three, don't worry, it's just equations. We have relative cooling power, RCP as one of the parameters that govern the properties, the change in entropy and temperature. So just remember we have three parameters if you're not a scientist. And what we're going to now do is then do our chem data extractor thing. We're going to mine the literature and find all materials that have reports of any or all of those three properties. Right? And we'll see what we get. So if we do that, there aren't actually that many. But there's just under 3,000 materials and each of them have those number of the individual properties. Tc, by the way, is actually now the curie temperature. That's because we wanted to find permanent magnetism right down at that temperature. And so that's what we got. And actually in that sense while we got this database that didn't give us a brand new material that was suitable for the liquefaction temperature to store hydrogen. But that's okay because what we can do is having got the database we can at least make a regression model because we've got data now with structure and property information and so we can apply machine learning of making a regression model to link structure function relationship for magnetocaloric effects. So that means that if we've come in with a new material then we can use this regression model to predict the magnetocaloric effect properties. Right? That's okay so now we need a new material. Right? So how do we get new materials? Well we turn to our machine learning world. Super geeky moment coming. And we apply something called a conditional deep field consistent variational autoencoder with UNET and crystal graph convolutional neural nets detector. If we're glad to know I'm not going to explain what that is. So what you essentially do is a way, it's a generative algorithm that essentially creates new 3D crystal structures hypothetical ones that could then be fed into this prediction. And the way it works without really going into really detail once the speech me offers I've been explaining to them is imagine you have in fact the imaging world kind of do this.  It's a bit freaky sometimes. It's not quite like that but it's sort of like that. In essence because all you do with molecular structure is an image at least you can treat it like an image just these three dimensional coordinates. Don't worry about bond lengths and bond angles and all those things. It's just see it as an image and if you treat it like that where you can make essentially a data distribution that's representative of molecular structure or crystal structure in this case and then you feed in if it's something that's close to your target material so for example with magnetocaloric effect materials we knew that say hoysler so a type of structure like hoyslers or perovskites or cubic materials tend to make good magnetocaloric effect materials. So we can say well let's take one of those that is known and then just sort of send it through this kind of what they call the latent space and then sort of go through this sea of sort of representative data distribution and then it will sample it and subject to a standard deviation depending on if it's a big standard deviation it will allow the input structure to be perturbed a lot if it's a small one it won't be perturbed so much and then it will output a whole load of new hypothetically generated crystal structures that most of which by the way might be totally unrealistic because it doesn't know anything about what's realistic as a bond geometry but that's okay because a lot of those will then get screened out because you check the energetics make any sense and if they don't you just throw it away. So we've now got this way of making hypothetically generated crystal structures and once we've checked them for energetics they could possibly be real and they're different from the input you put in by enough that you're happy that that's different and new and so then with these new materials you do some further screening to check a few things you check its ferromagnetic for example you can check its phase stability that sort of thing and again this kind of inverse pyramid pipeline that goes from say 1,000 down to 30 and then with those leave materials you can then apply that regression model that I talked about that we got from the experimental data with chem data extractor because that links structure and function so we can take now our new hypothetical molecules sorry crystal structures and predict the magnetocaloric effect properties and once you've done that you've got a prediction of new material and then we have to seek out a way to synthesise it and depending we can do that we can experimentally validate that material as a magnetocaloric effect i.e. a magnetic refrigerant so here are the results the blue are the known ones from the literature that we grabbed these are the two of the properties the relative calling power on the vertical axis and the curie temperature on the horizontal so that's the things that were known in blue and the red ones are predictions brand new materials one of these is this one and if you know your periodic table you'll know that Pm happens to be the one length allied material that's radioactive that's annoying isn't it so chem data extractor doesn't know anything about safety by the way so we can't do that but we could substitute out the land slide maybe and we were quite keen because that was quite a nice and nice temperature range for liquefaction of hydrogen otherwise but you could also go down here and pick something slightly different and so we're now thinking about making or trying to make that and here I am at the neutron and muon facility waiting with my sample stay ready for it to go on but we haven't quite synthesised it yet and so that's really all about I have to say about hydrogen we were up to on our discovery end from this project but also just so you know with other predictions we could think about room temperature type magnetic refrigerants because they could be used for a different type of fridge and we've got predictions there which we're also trying to make so that's number three out of four and now we turn to batteries and this is a picture of a mountain where you've had electrode in the sky and electrode on the ground and you've had an electrical discharge obviously that's just lightning this mountain's been actually struck by lightning and it's on fire it's a few years ago now does anybody know where it is? is it obvious to people? it's interesting I'm having lots of shaking heads from scientists as well because that's it bigger any further thoughts from the scientists particularly the neutral of the synchotron people? I think I heard it's going over that's the mountain here that's on fire and this is the European neutral facility and this is the European synchotron radiation facility next door to it anyway just to give you an excuse to show you the European facilities and so nature's form of electrical discharge but let's now think about batteries so we're going to do you're getting quite bored of hearing this probably now we're using our chem data extractor text winding tool to mine the literature in case you haven't got the message by now and in this case we're going to mine materials and the devices information right so we're going to mine the capacity, the conductivity the voltage, the energy, the columnic efficiency and we get nice distribution grass like that and again you see this weird rounding effect with the unitary things coming out again because it's real experimental data now in this case we grabbed all the material and device property information but what the computer couldn't do or what chem data extractor couldn't do was distinguish between whether the material was anode, a cathode or an electrolyte it got lots of materials and all the device properties but it didn't actually, it couldn't classify which ones they were because that was never really programmed into chem data extractor so we then thought well how are we going to do that and so what we did is having got that database of nearly 300 ish data records from chem data extractor we went back and we sort of reverse engineered it so we took, we had a scientific corpus that we fed in right that chem data extractor process and we took from it the papers that had a successful hit from chem data extractor things that contained batteries information to make our database and then we siphoned off that corpus now we've got a very battery rich corpus right of data and we then fed it into a different pipeline and then we explained what the pipeline does so these are our battery rich data corpus and we then are going to make something called a data model so this is a different way of mining data because what you do is you, this comes actually from a technology that's come out of Google AI in fact it's often the basis behind the Chrome search engine actually and what you do is you essentially you make, you turn you take those sentences that I talked about that's how you remember how chem data extractor works where you have these sentences and then you pull them aflart and all these things but instead of doing that what you do is you make vector representations of them so imagine a phrase like the cat sat on the mat and I write down the cat sat on the mat two times identically and then now you'll know that the subject and object are the cat and the mat so that has a high correlation between the cat and the mat so you can build a bipartite graph so you can link those two with a high correlation and so you can start to get contextual information from that sentence by essentially relating relating the information that you know is highly correlated together and the computer can do that by making this vector representation and having made the vector representation it will then feed it into a neural net a very deep learning neural network which then subject to all sorts of weightings will train all of that with all the sentences that you put in right so in some sense it's different from chem data extractor in all sorts of ways I suppose but in one particular way which is that instead of extracting the chemical and property information you keep all of the sentence information all of the corpus all of the words everything you put it into a model right now use that word very carefully so now what we've made right we've trained a network to build a model of all of that corpus with all of the context of the information from the sentences and so it's now what they call a language model right simplified a bit but that's basically what you're doing now that's really important distinction between that and a database a database is a very static thing right which you look at and you can do analytics on but now we have an interactive model right that's why I'm calling it a model and the way to think about it in my head at least is to think about it as your computer right so you have a motherboard right and that's the core kind of operations of your computer and you have lots of peripherals like mouse, your printer keyboard and so on and so these are the peripherals and you can put things together and the mouse has a different function right so I can plug that in and make my motherboard do different things and it's kind of like that as a sort of concept of framework so we've got this model right this data model is a language model and I'm now going to ask I want to ask questions of that model right because it's interactive I can do that so I want to say of course what is the anode is it an anode is it a cathode or is it an electrolyte from the material standpoint so what I do is I make a whole load of questions up just that have designed answers like what is the anode what is the cathode, what is the electrolyte and so on I can make more complex sentences of course and so you make another database just of questions and answers and you make it for the domain of interest the materials domain of interest the battery domain and then you can also mix that up with lots of generic question and answers pairs right just about normal English language and then you put it all together and then you can ask the model questions and so you in this case we want to know what the anode cathode electrolyte are as a classification so we ask those questions about that and then we grab the answers and then we can then classify whether the materials that went in the original database are an anode cathode or electrolyte hopefully that sort of makes some sense and this is what you get and you get kind of what you'd expect from what is the anode, what is the cathode and what is the electrolyte that are so that's classification and we can go one step further than that with this data model concept and this is really a paradigm shift I think between that and databases and we can not only have a question and answering module but we could make a natural language processing model a text mining module right so what if we actually blended chem data extractor, a chemistry aware natural language processing model a text mining module with this new technology from Google AI and make battery data extractor you'd say I haven't got much imagination with my names by the way and so this then just published like eight days ago or five days ago or something, I get lost, sorry this is the first property specific text mining tool for auto generating materials databases and we've done it on batteries and as I say it blends two different technologies to hopefully get the best out of both worlds it's also good I think because it's got a probabilistic nature about it because it's a model you can actually get confidence scores you can actually get probabilities of each data record that you obtain to know how likely is the question that I ask going to be right now that's really important because you know how does somebody know whether they should trust my database that I've got that was auto generated with chem data extractor right I mean we do experimental validation obviously but you know to get that trust we need to think about real probabilistic confidence scores for all of the data records so I think that's a good thing the fourth thing there is that we have now a new way to interrelate material and property text during data extraction right so imagine you've got this is a really big problem by the way imagine you've got a scientific paper you want to mine the chemical and property information but let's say the chemical name is on page 1 of the document and the property information is say on page 5 and in between on pages 2, 3 and 4 we've got a whole lot of other chemical names and a whole lot of other property type things how do you know that the chemical name on page 1 and the property on page 5 are related not something in between was related to either of them right so what you can do because you have a model not a database anymore you can ask if you're careful the right sequence of two questions that by definition interrelate them by essentially using a sort of analogue version of Bayesian statistics it's a conditional probability if you see how it works let me give you an example so let's imagine I want to find a material with a voltage of two volts just for argument's sake so I am going to ask two questions in this sequence what is the value of the property name property name is voltage and you get an answer you're just dealing with the property at the moment right so I said the answer was two volts so you can find that because that's easy it's just looking for one area in the document but then we say which material remember we want that link between material and property which material has a property name the voltage of the answer to the previous question so there's your conditional that's your Bayesian bit and the answer to the previous question was two volts so the question becomes which material has a voltage of two volts and that puts that into relationship into analogue sort of a geeky very geeky way of doing Bayesian kind of statistics and so that was published recently on the front cover of chemical science which is the Iwasa society of chemistry's flagship journal so leading on this is really then thinking about where we're going with all of this and you can see that you can build up all these different modules having different peripheral functions just like the mouse and the printer and so on that have different functions with the spectral motherboard of your computer in this case the analogy is the data model and you can then make it do lots of different things right so that's I think where we really want to be going moving really away from actual databases and moving into this more interactive type of zone so that's the software side of things and I just want to close on thinking forward also about the experimental world this is a picture of the new new Ray Dolby centre in Cambridge University which is nearly finished and this will of course allow us to continue doing the mod cons of this is where the physics department and others will be housed so we'll continue to have the mod cons of the experimental validation side for example and this is the Rutherford afternoon laboratory to which I'm 50% seconded and it's already got these facilities and others and there's Diamond, the X-ray source the Isis, the Neutron and Muon source the central laser facility here and in future it will also I believe have all these extra things so there will be a new development in a laser facility there's already nine new instruments being planned for the existing Neutron and Muon source and I believe there's a possible option of a new Neutron and Muon source called Isis 2 over here maybe of some other location possibly so that however it reminds me to say that this site like many big organisations across the Rutherford afternoon laboratory is particularly large but like many big organisations they have a plan to go next zero by a certain year that's a really massive challenge and I've talked today about a design to device pipeline but that's a linear pipeline right but if you really want to go to sustainability then you've got to adopt the circular economy right actually it's more of a butterfly shape so our design to device is kind of here but we've really got to build an ecosystem everybody talking you know talking to people on the county council dealing with the recycling sort of geeky scientists and engineers who are designing and making new materials we've got to build that ecosystem that's a really different way of working that we've got to adopt so you can tell again to the end of my lecture I'm going to get a bit more light hearted now we can call the user support with our bat signal that's us in the new facility the lasers reaching out and they will call on the user community which has an international reach and we can make our circular economy and working even more closely together across the facilities we can continue to do Olympic science for a sustainable future and now for the grand finale I want to thank people and I want to thank people properly because really this is a celebration of everybody with whom I've worked and so I wanted to do it properly rather than just put aside so I made you a short video to thank everybody so here goes it's fantastic thank you so much it's really special and different anyway questions for Jackie there are microphones and remember there's an audience out there as well so I'm going to stand in front here so I can be seen thanks Jackie a number of your databases have 100,000 papers or so how long does it take a computer to read 100,000 papers and is it a laptop or is it a supercomputer so we've got two different ways of answering that the time taken to read or the documents is not so long what takes the time is actually the data cleaning process once you've mined the data you don't just get what you want you actually kind of get lots of educations things that don't quite work and so you have to spend a lot of time then actually cleaning the data so I don't want to pretend that here the data extractor is something you can just press the button and out pops the database to give you a real example of it in practice a fresh PhD student from say my group would take probably about two years to get a database that's in published work so that's the sort of and obviously they get quicker because they've learnt at that point but that would be coming at it fresh so we do use supercomputers for sure and certainly it's a lot faster and for the really big runs we will use a supercomputer but you could in theory as long as you've got time you could do it on a regular desktop computer in front Thanks Jack it was a great talk you talked about extracting data from the scientific literature I guess within our facilities nationally and globally there's huge amounts of data and I just wonder what your thoughts are on extracting that data in terms of challenges and opportunities because clearly a lot of that data doesn't end up in scientific publications so I guess it depends on what condition the data are at least what stage the data are in their production so you've got raw data which we couldn't realistically mind because you've got to process it first so that's where you have to reduce your data and analyse your data so that it becomes something meaningful that you can relate to the rest of the world so that's the first thing once it becomes processed data then you would be in the position where you could maybe publish it or you could not if that data were available to the likes of us then we could actually take that data but we would have to make it into some sort of internal framework I think that we would pipe that into the database format but there's no reason you can't put a side arrow into that pipeline right of course you're on a better ethically than me how ethical is it to take somebody else's data that they didn't really write up and then use it without maybe asking them but after three years I believe it becomes open access anyway so we get into sort of policy issues and even possible ethical issues so I think we have to think about that and there is something called data site which is the sort of framework that actually they've been very active at the Wella Fflaffon Laboratory in making that actually allows you to source if it's old enough more than three years you can actually go in and find even the proposal that people wrote to do the experiment the metadata that was used in the log files if they're electronic, if they've written if the users wrote them down and then the actual raw data and if you really wanted to after three years you can go in through this meta site database and you could actually process it yourself but again we get back to that ethical question somebody else did the experiment maybe they were doing a PhD they finished, they left should you go in and process it yourself and publish it and who's the publisher and who's the author raises them interesting questions but yeah I could actually ask one myself as a computing person I could see quite a number of tools that a computer person could construct from our end of things which might help so for example a programming language designed for this sort of domain does this happen or would this be unusual I know it would be helpful because I can think of quite a number of things which would make your life easier so it depends what you mean by that programming languages should be the thinking of other things we program everything in Python that's partly just because that's what a lot of people know and because we want the consistency across the board but for example we could think about probabilistic programming languages so the likes of Julia for example and that I think could help it might make more things efficient and certainly if we get to actually it relates to Russell's earlier point if we're using supercomputers maybe we can make it more efficient so that we could do it even better on a desktop ultimately everything's, as you know, Moore's Law exponentially it seems increasing I think there is interesting stuff but anyway any more, Andy Thanks Jackie, that was marvellous I'm thinking about the underlying physical mechanisms that make materials exceptional so you can identify or point towards an exceptional material you had some in your plots can your system give you any insight into what is different about the mechanism in those materials that makes them exceptional or is that still a job for humans? So yeah one thing I would say is I don't think that humans are going to become redundant I think there's some of the really novel stuff we will never get because we're predicting based on trends so we would never predict to the new quantum technology for example in fact there's not enough data now to make head roads in that but I think I think there are edge cases and you could look for outliers you could use chem data to find all the regular stuff and then say what's this thing over here and is it just a totally duff piece of data or is it actually really really special? At the moment we might look at the outliers but we sort of pre-programmed a little bit to kind of think that they're outliers and therefore they don't count but we could look at that differently for that purpose Over there, thank you very much Wondering how reliant you think we are about changing trends in how data is actually published into the scientific literature are partly standards of English and language but also aspects like now a lot of papers have far more material in the SI than is actually in the paper and I don't know whether you are mining the SI in the same way So by the way there are quite a few of the publishers in the room so it's a very relevant question I think there are differently ways that regulation could help if you take the historical example of crystallographic data and it's been for decades in a situation where it's been mandated that you can only publish crystallographic information if you include a SIF the crystallographic information file as part of your submission that's not the case for almost anything else and so there's a lot of data that are hidden that actually can be really useful that's one thing to say that with the crystallographic information so of course it's a burden on the publishers but then potentially if it was regulated people might do it the chance of anybody doing it voluntarily I think are actually quite slim despite best intent and I can speak from personal experience when I publish a paper the last thing I want to do when I'm looking at the submission I was like I've got all these files and now I've got to produce a crystallographic information file and I kind of curse a little bit but I still do it because it's mandated but it wasn't and so for all the best intent in the world I think we have to find a way to regulate that sort of process with regards to supporting information you're right a lot is increasingly going into supporting information and the problem with that from my standpoint as a data extractor if you like is that it's all PDFs and the problem with PDFs is they're really hard to read we've just made a PDF data extractor and if you saw it in the clip as it was running through at the end we made that because we can't access most of the supporting information and so we want to be able to do so so PDF data extractor is actually a code that will actually go into the front end of the PDF data extractor so that PDF extractions will be better it's still not great because the PDF extractor is of course optimised for markup language extraction because it's easier to access by far so we have to think about that and I'm always very happy to talk to publishers to see if we can work together to find a way through to improve that process for everybody


De har jobbet hårdt til å få mer ekipement, mer ammunition, og de har skjønt en vildighet til å forstå, men å fortsette denne veien, og det er ingen indikasjon at President Putin har skjønt denne overallt aimen av denne brutale veien against Ukraine. Så vi trenger å være prøvd for denne veien. Vi starter med finansjordene. Henrik, finansjordene. Det er en spørsmål for President Mechel i oktober 2021. The European Union will keep doing everything in its power to support the brave people of Ukraine. We will keep the pressure on the Kremlin for as long as it takes with the biting sanctions regime. We will extend these sanctions to those who militarily support Russia's war, such as Belarus or Iran, and we will be coming forward with new sanctions on Belarus, answering their Belarus role in this Russian war in Ukraine. I have said many times since the beginning of Russia's invasion in Ukraine that I think that Ukraine should get all the necessary military equipment they need, and they can handle to defend their homeland. And this means, of course, advanced air defense systems, but also other types of advanced military equipment, as long as it is necessary to defend Ukraine, because they also defend the basic principles of the UN Charter, of the fundamental rights and of the international law. It also recognizes the value, and I welcome the new announcements just over the last days by the United States, Germany, France, to also provide new types of armored vehicles, infantry fighting vehicles, and other types of armor to Ukraine. I welcome the focus on systems, but I think we need to also realize that this is not only about adding more systems, more platforms, more weapons, but also ensuring that the platforms, the weapons we have already provided, are working as they should. Together, the EU and its member states, Russia's war against Ukraine has had two unintended consequences. It has strengthened both our organizations, the EU and NATO, and it has brought us closer together. To address, in particular, the growing geostatic competition, we are determined to take the partnership between NATO and the European Union to the next level, to address in particular the growing geostatic competition, resilience issues, and the protection of critical infrastructures, as well as emerging and disruptive technologies, space, the security implications of climate change, foreign interference, and information manipulation.


So, hi, hi everyone, thanks to be here with me. So my presentation is about the community initiative, Wikilev's literature. So let me introduce myself first. I am Nitesh, my username is Nitesh Gill. I am a part of Punjabi Wikimediance User Group since 2015 and my primary project is Wikipedia. My main focus is on women-related content on Punjabi Wikipedia. And I have started my 100 Wikidays in 2017. It was my first time, but I decided to continue it. And I have done more than 2,000 days. And I already created and developed articles under that challenge only. So, yeah, it's about me. And I am here to talk about Wikilev's literature. So this is a community initiative. So as you can like understand with the name of the project. So yeah, it's just a community initiative. So we started it with an aditathon. So our focus was on literature only. So our community can create and develop and edit pages on Punjabi Wikipedia related to Punjabi literature. So it started in 2021 within the Punjabi community. It was for only 15 days. And at that time, all the communities said together and tried to create some content together. So next year in 2022, so we decided to bring more people to this initiative. Then we approached three more communities. We tried to approach more communities, but three communities responded to us. Those communities were Assamese community, Udia community and Santali community. These communities are also Indian communities. So these three and fourth one was Punjabi community. So these four communities planned accordingly about Wikilev's literature initiative. And we decided to do this work together. So like we have planned and we prepared our own list. So then like it started with because Punjabi community is not that big community, but there are really amazing editors. And those editors are mainly for editing Wikipedia and Wikisource. We are also editing other projects, but our main focus is on Wikipedia and Wikisource. So because our group is also a literary student group. So means I and my friends are a student of literature, Punjabi literature. So we decided to do something for literature. Gaurav Jhamat, he's also a Punjabi Wikimedian and I initiated it. We found there are many important articles which are missing in Punjabi Wikipedia. So we decided to start a start a kind of project or campaign. So we can bring our Punjabi community members to create create articles. So we only start with our Punjabi only. So we discussed with community community supported this initiative. And then we did an experiment, I can say. So that was an experimental event. So we also need some clarity to do further. So then that was the result because it was the 15 days at the town. So only a few people participated in it, like six people. And then we only had 185 edits. But yes, after this initiative, we get to know how we can improve it. So then in 2022, here are some results from Assamese community because I already mentioned we collaborated with three more communities. So this slide is for Assamese community results. And then it's for Punjabi community and then Odisha community. And then it's for Santali community. So for these results, in 2022, we decided to use fountain tools so we can get to know how many users are working with us, how many articles were created under this initiative only. So we also tracked with Wikilov literature category. So you can go through these results. And so I am here on behalf of Punjabi community. So we want to continue this initiative. That's why we are presenting it here. So that project, we can add it at a time. We can add it four projects under this initiative. Wikipedia, Wikisource, Wikidata and Wikimedia Commons. So on Wikipedia, we can create, improve and develop or expand articles on Wikipedia. Also, we can add photographs, needed photographs in articles. So we can complete our good articles so we can improve this article's quality. Then also on Wikidata, we can create and update an item related to literature related items. So yeah, it's important for Wikidata as well. And in Wikimedia Commons, we know Wikimedia Commons is really important for us. And we can upload our photographs there. We can add videos, audios, such as writer's interviews, because we are already planning to do some interviews of writers. So we are also considering it in WLL. And we can also focus on Wikisource. So for Wikisource, we can do partnerships. We can upload text. We can digitize the text and books. So we can edit pages on Wikisource because we are already doing Punjabi Wikisource. So yeah, we can also edit Wikisource and we can transcribe our books on Wikisource. So yeah, these kind of work we can do under this initiative. So yeah, what's next? So as I mentioned, I am here for more collaborations. I want more, not I, we want Punjabi community wants more collaborations from the Wikimedia movement. It's because we needed that kind of team, an international team, so we can sit together and we can find challenges, we can find what can we achieve, what we will cover with it if we are going with WLL. And we want it like as a campaign and we can do it as annual activity for a month. So yeah, this is from my side if you are interested to collaborate. If you want to know more, all the material is on Metawiki and also you can contact me. We can also have a chat. You can meet me in person. And also you can contact me on Metawiki or on Wikis. So my username is Nitesh Gill. You can also email me at gilteshu at gmail.com. And also we have in this campaign email address. So you can email us on Wikilev literature at gmail.com. So yeah, thank you so much for joining this session. Thank you. If you have any questions, so please go ahead. I have a question. I'm Asaf from the Wikimedia Foundation. I took a quick look at the project page on Meta. And first of all, it says that it's about Indic literature. If you are now asking for more international collaboration, does that mean you're expanding to all literature or do you just want international help with Indic literature? So as I mentioned, we started with Punjabi literature only. Then we expanded Indic literature. Then now we are planning to bring more people if we can expand it with more literature. So that page needs updating. My second question is you mentioned Wikidata as one other possible. Right now the rules, the competition is mostly about Wikipedia. There are rules about what the article should be like, et cetera. If you want more people to document books on Wikidata, one thing that would help is tutorial or step-by-step instructions on go to your library, pick up your Telugu or Tamil book, and here's how you get it on Wikidata. Here are the fields. Here are the properties. Make sure you don't do this but do that, like a tutorial for people who are less versed in Wikidata in general and specifically in describing books on Wikidata, which can be a little tricky, what to link to what and what to do with the addition details, et cetera. So I think that's instructional material that's missing to really help people join the project. Yes, right. Thank you. Thank you so much, Asaf. Yeah, I will follow these, your suggestions. Thank you so much. So, yeah. Okay. Yeah. If there's time for one more quick question. Hi, I'm Emma. I work mainly in New Zealand literature on Wikipedia. I'm just interested to know, because you said in your first, the first round in 2021, you didn't get, you know, you had six people editing. How did you then, in 2022, try to get more people involved and engaged? Sorry, could you just repeat your last part? Yeah. Just in 2022, you didn't have a lot of luck. The first year you ran, you didn't have many participants. Then you had more the second time around. Was there anything you did differently to get more people involved? Yes. So, for like 2021, we had that project within Punjabi community only. Anytime we approached Indian communities, other communities, then we tried to bring them so they can give some more ideas and we can do it like on some like larger scale. So we approached them, we emailed them and we also like had some Google meets with them. So yeah, that was the, it had like this, we contacted people and for Punjabi community, we had some like online meets because we are already doing our monthly community meetings. So we discussed everything there. So yes, and after our first 2021 WLL, after that, we discussed with community, there are the gaps and we need to do these kind of things. Then we sorted it out and then we came with more results. Thank you. Thank you. So yeah, maybe we can wrap it up. So thank you so much for joining the session and thank you for your questions and thank you for your suggestion. Thank you so much.