 Ready? Okay, before I get this talk, I'm asked to announce the winners of the poster award. So we, there were tons of posters, astronomical number of submissions for posters, right? There were staggering seven posters and it was really, really difficult to evaluate this so I couldn't do it on my own. So I was helped and assisted by Lambert Heller, Alexandra and Benedict, by the way. So if you could all give them a hand, that would be great. It's an arduous task to evaluate this poster and all seven of them, if I had money, I would give it to all of them. But since I don't, I'm a poor professor from Switzerland and London. So the two posters that we selected as some of the best, all of them were pretty good. The averages were pretty close by, but the two were fantastic. And the first one is by Moritz Schubels, repurposing open source tools from Bella Gibbs Wuppertal Group, by the way. Give them a hand. Yes, like present his poster tomorrow morning. Yeah, he or she has to present it Moritz. I hope it's a guy. And the second poster is to Eva Kelmar on blockchain readiness from the TU Doft. So, great. So, something is happening there. Well, it's getting fixed. Let me introduce myself. My name is Lauren Stodgenden. I am a professor of neuroscience at the, at a King's College London. I used to be a professor at the University of Zurich. I just moved to King's to be the director, deputy director of the Dementia Research Institute, which basically aims at defeating dementia in five to 10 years. So I'm a practicing researcher. I work with data, scientific data every day. I work with a lot of researchers who are fantastic. But this also allows me to see the good in scientific data, but at the same time, there are not so great things in the academic world. And so, Sunke has asked me not to give a scientific talk today, but he in our, he's asked me to give a talk on, on the two startups that I have, thank you, which are Science Matters and Eureka Blockchain Solutions, which I will touch upon it. But we were to tell you a little bit about the epistemology of science, the way we do science and the reason why we do the things that we do. And so before I could start, let me ask you, how many of you know this story about, or the jokes on, about two cows and capitalism? Oh, nobody, that's great. So, so that's the, there's this traditional capitalism. As you know, there are two cows. You have two cows, you sell one and you buy another bull, you herd multiplies and the economy grows, you sell, sell them and then, and then you multiply this income. This would be the French. You have two cows, you go on strike, organize a riot, block the roads because you want three cows. This is the Swiss capitalism, which is your five thousand cows. None of them belong to you, but you charge the owners for storing them. You know where I'm going with it. I'm Indian. So, if you're an Indian corporation, you have two cows and you worship them. I want to, I want to tell you the, the insane mad codestan with John Tennant introduced today, which is really you have zero cows, right? You give one arm and a leg to people called publishers along with data, idea, all of that. And they might, you know, they might publish your data. And this is an insanely capitalistic market. And that's like $25 billion is just only in the English speaking, English speaking, scientific publishing and academic publishing. And there are like more than 50% of the margins. And I want to tell you this, this, this, there were these two tweets. There's a guy called William Morgan. He said, two academics walk into bar. They bring their own drinks and then they pay $5,000. And then John, John, is it, are you here? He said, that's not really great. But this is what the metaphor that he said this morning. And this is, it comes almost close to what we are dealing with every day. Not only we as scientists in my lab, we've not only read these papers, come up with great ideas, make observations, great hypothesis. Not only we ask and write a grant, we have to write this grant. This is like a strenuous process. We have to write a grant to the funding agency where you as taxpayers money, taxpayers give us the money. And we, we, we curate data. We, we, we work on experiments and we make discoveries, findings. And then, and on top of it, we provide this data to a publisher who then says that maybe this, this can belong to a scholarly space. And we have to pay something like $5,000 to, to the publisher. Not only to, to create this knowledge. There's this barrier to create. But at the same time, there is also a barrier to access knowledge. My university in Zurich pays around $5 million per publisher per year in order to just give us researchers access to the knowledge that is created by taxpayers and, and researchers. And, and, and so that's something this, this is extremely important to know the economics behind publishing. And, and, and, and I will tell you over the time that it's not just the publishers who put these barriers, but there is also another stakeholder who also puts barriers for this open science. And, and, and if you were to make science, let's say, even if you pay $5,000 per article and $5 million for, for access, did we make science better? Did, did I get a return on my investment? And, and is it, is it better? Actually, not really. Science is not entirely a, a, a rosy. There is high degree of irreproducibility and, and, and, and, and lots of researchers acknowledge there is a reproducibility crisis. Not only, it's not necessarily that we do science, the science that we do now is worse, but it's, it's a combination of, of somehow irreproducibility is higher. And at the same time, somehow we also came up, we are coming up with tools to detect fraudulence, detect misconduct better. But it nevertheless says that there is a crisis, there is a crisis in terms of irreproducibility. And, and the irreproducibility could come from many different ways. It comes all the way from kind of a, how do you say, a lack of robust validation of our science. There is somehow we rush to publish. There is something that, that we don't really robustly validate because there is an incorrect analysis, et cetera, to some degree of something so stupid like this. This is, this is, I'm sorry about that. This is from a group in Malaysia which published the cell biology paper where they show that, as you can see here, there are these cells that named one, two, three and four. And their idea is to say that the, you know, under different conditions, all these cells have different shape, right? I think this story went down like this. They started to, they submitted the paper, they submitted the paper with only four cells saying one cell, one looks like this, two looks like this, three and four look like this, but only having n is equal to one. And probably a reviewer would have asked, you know, it's not, it's not really scientific if you gave me only one cell to show that, that, you know, one cell looks like this. You have to show more cells. The researcher said, hold my beer. And then they went and clonally copied these, as you can see here, if you can see it, they cut this one cell and then pasted it on a black background and then showed it. Now you have more cells to show. Right? You can debate why they did what they did. And we will come back to this question as to not only questioning the morality of the researchers and why they did what they did, to also ask about the system that somehow demanded this dishonesty in some ways. And we'll talk about this particular issue today. And so it goes all the way to going from non-robust validation of truth or the lack of consensus that will allow me to validate what I will call it a scientific fact to something as really not very intelligent as this particular thing. And this is due to the fact, why do we do this? Why do researchers want to publish something? And this is this idea that we need publications. For researchers like us, publications are the currency for in the ecosystem that we live. In order for me to get a grant, in order for me to employ a PhD student or a postdoc, in order for me to feed my children, I actually need publications. This is the currency that we live in. And as a result, as soon as you have something like this, you end up manipulating, end up being non-robust. And an impact factor or the journal that has highest number of citations is called the impact factor. And if you could publish there, then many things are granted in a way. When I was finishing my postdoc at the Max Planck Institute in Dresden, I got a phone call, which I will never forget. And there's also the phone call that actually changed the way we design, I design and also it's the birth of science matters idea. A researcher, a very well-known researcher from the University of Zurich called me and said, like, you know, what, Laurie, what, what are your future, what are your future plans? So I don't really know. I'm just, you know, finishing my work. I was working on Alzheimer's disease. And, and he said, so what, what's going on? And I said, you know, I have these two papers, and these two papers are now under revision. And I don't know, I'm just going to think about continuing this work. He said, this researcher did not ask me, what was the paper? But he asked me, where is that paper? There was, there was no discussion as to what the contents of my study or the science was. But the question was really merely focused on where this paper, these two papers were submitted. It so happened these two papers were in a journal called Science. And, and I said that this is, they are in science. And he said something interesting. He said, if you had these two science papers, for sure you'll become a professor at the University of Zurich. And I tell you what, it did happen. I did have these two papers, and I did become a professor at the University of Zurich. Without, without really knowing what the content of the scientific study is. And I tell you what, we often have these dilemmas in terms of how a measure for metric should be, what a metric should be. And we do this because we are all here, not because we think this blockchain is going to change our lives. We are trying to make the system efficient. We are trying to make our lives a little bit more convenient and making, making sure that can we compromise effort with convenience. And so impact factor came as one of these measures. But the way it has changed exactly what you say. And, and not only that this, this would make you a professor today if you had these impact, high impact factor journals, you could get discount on a barbecue in China. So this is an actual restaurant in China where it's, it's called Lancet Barbecue. Depending on the last paper that you had and its impact factor, you get discount based on the impact factor of the journal that you published. All right. And this is very, very true. And so question is, what is this? And I think we have to discuss this. We all have to discuss. We understand that we are here to discuss about the potential of the technology. But even when we think and execute, design and execute, blockchain technology today for science, we need to understand, as Ulrich said this morning, as John Tennant said, can we change the mindset of the people? Can we change the incentive structure of, of, of academia today? And, and, and as I said, if, if a measure becomes the goal, it actually stops being a measure, right? No matter whatever we do, if we, this is our goal. If, if my metrics, the fact that I need to get a cell paper or a science paper or a nature paper, if that becomes the goal, I think we are in a lost battle in, in, in academia. And, and, and we go all the way even to kill people. I'm not kidding. This is, this is there. There is, there is, there are people who kill. I don't want to sound like a populist, but our Fox News really, this is the, these incentive structures measure against some of these social, the, the social good that we look for. I am an academic. I teach a lot of students. And when Nature published this report that collected various interviews from senior graduates scientists, this actually shook me to the core. It says like what, when, when, in many labs, when people asked the incentives to be first can, can, can be stronger or it is stronger than incentives to be right. And these have real life consequences. When we rush to do science or our goal is really to have papers as opposed to doing science. The real life consequences could be really money that it's, it could cost us a lot of money. It could also cost us delayed progress, delayed scientific progress. I work in a field which absolutely does not have a cure till, till date. And this is Alzheimer's disease. And, and, but as you probably know, if you read news in, in any newspaper, this is what you see. There is a breakthrough. We keep curing Alzheimer's disease in every paper, every cell line, every mouse model gets, gets cured of Alzheimer's disease. However, when it comes to the actual numbers that the Alzheimer's association sent us two years ago, this is the thing that is an upgrade. We haven't cured that the mortality due to Alzheimer's disease is actually higher. And, and when you look at the irreducibility that happens in these fields, there is a clear, clear retractions and, and etc. So there is, there is a lot of the, the way we do is that we try to somehow cure these diseases in a preclinical model and in these papers and in these cells. And, and when you ask this question, why is that? And there is a company that lost a lot of money on a clinical trial that just failed. And when they, when, when this company's money fail, the clinical trial failed in this dementia field, people wrote to them saying like, you know, we knew that this is, this won't work. We knew that this target is not a right target. Why did you go with, and so the company asked like, why didn't you tell us? Why didn't you tell us that it didn't work? Today we have paper journals that allow only positive storytelling. There is only, if you tell, if you go to a journal and say, this didn't work, there is absolutely no place to do this. And that's, that, and, and, and we all, in my lab, if you walk into my lab, there is at least 90% of the data that talks only about negative and replicatory and, and kind of experimental and not storytelling worthy, but there is this top 10% that is like kind of a story worthy or, or sensationalizing the way we, the, the, where we cure diseases in the preclinical model that gets published. And, and, and if you're an Alexander Fleming today, if you are, you know, if you're whoever you are making these amazing discoveries, if you, if you're in a rural part in, let's say Africa or India or Asia somewhere, and, and you make these brilliant discoveries, today you couldn't. You have to tell a story. You would have to, if Fleming's discovery were to be published today, then, then it wouldn't be, because you have a, you need to tell what the molecule is. We knew the model, the exist, the, the identity of the molecule much later. There is even in life sciences, you need to tell the mechanism through which this observation happens. In this case, the fact that penicillin would work against bacteria through cell wall synthesis happened only 40 years later. And today, publishers demand that you tell a story behind these observations. So as a result, science is closed. It is, you see, it is easy for me as a researcher to blame publishers saying those are the people who close science. I also want to tell you, not only that there is a demand for storytelling in, in science, but there is also a desire from a researcher perspective to tell a story. So if I make this observation like Alexander Fleming did, I want to keep that story close, because I want that observation close, because I want to make sure I can tell, I can collect all these elements to tell the sensational story that will go to a nature or a science. I found that science matters a few years ago, and then whenever I talk about it, they often ask, if you publish that first piece, the novelty for the rest of that story is gone. Can I still publish in nature? And, and I think this is extremely important to understand that closeness in science, the fact that we keep science close for a very long time from making discovery to publishing or dissemination of science comes from both, both publishers as well as researchers. And the reason is that we have this desire as well as the demand for storytelling. We have to have all these elements, and the elements cannot be negative. They cannot, they cannot contain plot spoilers. I can't tell today that this work in the cell line, in the transonic mouse, this is the phenomena that works for Alzheimer's disease. However, when I change this model to another mutation, another thing, another, just a cohort, if it doesn't work, I can't say this because I will be shooting myself in the foot, right? Nobody is gonna, but that's what irreproducibility in terms of when I go to a clinical trial, the fact that it doesn't work in, in different human beings could also be the reason that it doesn't work in these different clinical models. And I said, so a few years ago actually in, in already in 2009, I started to think quite a bit about really on, as to why as human, as scientists, we have this desire, and why do we, why do we do things that we are not really comfortable doing, but we have to do. And the storytelling, for me, came up as one of the strongest points that defines why we do things that the way we do, and probably a cause for store, for irreproducibility. So we started to say, perhaps what we need is some kind of a Lego like, like kind of an Instagram-ish, but in a way that you build these narrative as you go, as opposed to tell the narrative from the beginning, because you are biased as a, as a researcher, as an editor, as a reviewer. And so we created Science Matters without a need for story, but just put these single observations and, and, and, and hypothesis, and they will be, there should be periods, should be open, peer reviewed, published, saved, extended. And, and, and the interesting thing with real-time publishing and these, these elements that, that you would have to do is, is really that it, there is no reason to prune the narrative. There is no reason to cut the narrative in order to tell the very cogent and the convincing and the positive story, but all these elements can be part of this. And not only that, that you could extend your narrative in your real-time, but at the same time others can also attach it as a result and narrative emerges naturally, as opposed to an author-pruned version of the science that we are being told today. And so, exactly three years ago, today, on the 5th of November 2000, oh, it's not 2018, it's 2015, we launched Science Matters. Today is also my mom's birthday, so I want to say it's my mom's birthday. And, and, and so today I will also talk about Eureka blockchain. That's the blockchain aspect of, of observation, publishing and reviewing and reading. And so we created Science Matters to, to do this where you could, you could, you could basically write up in, it's like an Instagram post, so to say, and then, and then it's sent to peer review. We use triple blind peer review because there is huge amount of bias in peer reviewing and then it gets reviewed and rated and it gets published in an open access manner, just by default open access. And so one could, as I said, you can, you can collaboratively, collaboratively attach all these elements that belong to the narrative. And now what happens is that you can see, now one could, one could look at what is, if we could qualify these edges, the way that these nodes are connected, whether this is an extending edge, if this extends, or it is a contradictory edge or a confirmatory edge, now we can do something even cleverer. We could say we can now make a reproducibility matrix, for example, to say which nodes are highly reproducible, which nodes are not reproducible. The, the, the left side could be an Alexander Fleming network or, or a double helix of a DNA network. That is very reproducible, but for some reason something is not reproducible. It could be technical artifact or it could be, there could be a biological or a scientific reason why this is, this is not reproducible. And now we can, we can, we can confidently attach some score to these things. And so we, we, we call it metric and this is the pattern that we applied for. Now we, we filed both in the US and in the EU. Now, just the last couple of minutes I want to say, with science matters we really attack this problem of storytelling in science, allowing researchers to tell the story as they make these discoveries and, and as a result we can address both this design and the demand in storytelling for, for storytelling in science and we could publish these elements together. However, there are still some of these problems that we don't address and I thought, and I thought that we could, we, there is still this long delay in publishing research even though it becomes a single observation and we definitely reduce it to, to, to a sizable number. However, there is still this long delay going from a eureka moment of the moment of discovery, right, to even putting them together. There is some delay and there's a centralized trust that we talk about that the authors manipulate or like the authors control this data, there is a centralized control by authors, reviewers, editors and the publishers. And, and, and the other thing is there is this absence of fair credit. You know, we do a lot of these reviewing work and editing work for almost no money and now one could think about using, using crypto economy or tokenization model, one could think about providing some fair credit and so we decided to, to use blockchain as in the parallel mode we have science matters up and running and it is there for three years and, and, and, and, but at the same time we thought we should do an experiment with blockchain based publishing and trying to figure out can we address these other problems that we haven't addressed using blockchain based solution and this is called eureka. It's eureka token.io, you can have a look at it. And, and, and in this way we could really immediately timestamp discoveries and that's why we call eureka. So as soon as there is, there is a robustly validated observation or a finding or, or a hypothesis or a retired professor immediately you could timestamp and, and, and then, and, and then the, the other aspects of, of, of, of the scholarly process including evaluation of both observations as well as preprints and we can work assignment and then we could also call, we could also award these research through involving funding agencies at the end one could, by using tokenomics one could also incentivize a replication. We could also incentivize people's research that got cited instead of just having as a reference we also reward a research that gets cited and then at the end we could also do data analytics on all of these data not the 10 percent the top 10 percent storytelling. Now, if you allow researchers to publish negative contradictory confirmatory observation that's like tons of data which we anyways have. We don't have to do this. We anyways have it and this is something this would allow us to have much better analytics on the data that we that belongs to the narrative and so this is, this is roughly the idea that one could use blockchain in most of these cases and most of the, we use both off-chain and on-chain aspects to, to carry out the, the research cycle all the way from electronic lab notebooks to data analytics. So I thought I could, do you want to help me a little bit? Just to show me the prototype. I want to show you the prototype and then so instead of doing an ICO and then asking for money and then doing, writing just the white paper we thought we'll just pull our resources and start to create because we have science matters already start to create a prototype that would be useful for both researchers as well as if you're in the publishing industry one could use this to yeah go ahead there's no sound but it's just a minute that's fine great so you could start this is Metamask you could, you could log in and you write your own article then it gets hashed identifier and then it immediately goes to an editor you can involve the editor invites reviewers this is a functioning prototype that we have at the moment and the reviewer provides the review on the what you see is what you get templates so there's no reason to send out as an email or print out and etc you could do this you want to fully utilize the digitalization technology that we have available this is what it is you can write a review long side and then you could either annotate we use junior researchers we apply junior researchers to annotate but at the same time also I have senior researchers to provide big picture reviews and both of them and so you get rewarded in and instantly you get reward and that's the beauty of this peer-to-peer system in terms of fully utilizing this P2P transaction of anything that has research value and that's pretty much pretty much this thank you thank you I still need to thank my team so if you don't mind so that's the these are I showed you already these are the token flow in the system and it's it's up so there are several advisors and both for Eureka as well as Science Matters and Science Matters is a team of me as well as Tom Sudof who got the Nobel Prize a few years ago from Stanford and he and I created this platform in terms of the functioning protocol and the team behind Eureka is Thomas Bocek who created the P2P network he's a professor at the University of Zurich and now at the highest Hochschilder-Rapperswell and a fantastic team of researchers in Switzerland where Luca Spalloni he's the developer and severant and on the editorial side we have Jan Lottwal who is who's the editor-in-chief of the content management we have Isabelle Andrew Tamara TJ and several others in the team and we have pretty good support from universities as well as now the Swiss National Science Foundation we are having discussions whether we could we could use this for even research data management finally this is my end I grew up in Islam in India I want to tell you this I grew up without electricity today I'm here and the reason is I had free access to opportunities I had free access to education it is immoral to put barriers between humans and knowledge be it for access or to create knowledge thank you thank you thank you thank you very much Loi are there any question comments? yeah okay thank you for your Berlin presentation my question is how is this talking reward system working actually what is a participant rewarded for and how does this token which is as I see is more or less a symbolic or arbitrary thing is transforming to some kind of real-world monetary value so the token in terms of reward it happens at several levels one is that if you are a reviewer today you don't get any any pay you don't get any money from the publishers or anybody who asks to to review so immediately you get as soon as you provide valid review you immediately get some of these tokens as a reward at the same time if you are an author and you published a paper that gets reproduced by somebody whether they reproduce it in a confirmatory or a contradictory way that's that that again gets gets rewarded but as soon as your paper gets replicated these authors who replicate your paper get rewarded right whether they say it negative or positive because we need all of this data and so we don't punish but if the paper gets positively replicated then the original author also gets some of these tokens because that's we want to incentivize anything that can be replicated but we don't want to punish or penalize the author whose research cannot be replicated because we think punishing is not a good means to to incentivize de-incentivize and the third thing is that if you are an author and your paper gets cited you get rewarded so these are the token systems and the token ecosystem and if you are a funder you also get you can fund through tokens if you are a grand administrator you can fund through some of these tokens so you can you can buy Eureka tokens and you can give it to give it to your grantee do these tokens trade on the market what's their value or are they just nice little badges for honor the idea at the moment is not really to have a market value but at at some point it will be in the exchange so initially you want to making an internal market of scientific value that's right that's exactly the ecosystem is built in Eureka we create an ecosystem that would be helpful for the researcher so that the 10 billion dollar that we use for just publisher or 25 billion it goes back to research that's our goal we'll get into the crypto economy for Zines tomorrow yes and Zines publishing yes tomorrow and I'm very excited about both sure yeah yes I say I say one more quick comment question yes how do you build narratives with your system sorry how do you build narratives with your system you're talking about narratives so the as you saw as soon as the the nodes in the network becomes something like seven or eight an arbitrary number that contains extensions replications then we invite all these authors everyone contributed to what we call as matters narratives this is the separate section which is more of a review and I think every journal should be more of a review journal when they when there is a narrative where the single elements are reviewed and rated without the story context and only then we could assign validity to to the scientific data as opposed to getting biased because of the narrative so the matters narratives does not exist at the moment but if you could see this is in in the pipeline as soon as we have these elements let's say each each node is extended to seven or eight containing all these elements we will have matters and we think storytelling is a part of science story we don't science matters logo as stories can wait science can't so we don't say stories don't matter stories can wait so that science takes the takes the priority great yeah thank you thank you very much Lori