Around 1745, Euler mentioned he had a proof of the fundamental theorem of algebra, but failed to publish it. In response, Jean-Léron d'Alembert published his own proof in 1746. D'Alembert's proof relied on several questionable assumptions, so it is not typically regarded as an actual proof. However, it used some ideas that were foreshadowed the modern proof using complex analysis. Euler eventually published his own proof in 1749 as researchers on the imaginary roots of equations. Euler's proof relies on some elementary results from the theory of equations. The most important one is that given the factorization of a polynomial, the coefficients of the polynomial can be determined by sums of products of the roots. A few others, an odd-degree polynomial has at least one real root, and an even-degree polynomial with a negative constant term has at least two real roots, one positive and one negative. Euler also made use of the fact that in an nth-degree equation, the n-first-degree term can be eliminated using the substitution y equals 1nth-a. So let's assume we've already done that to an arbitrary quartic, which gives us a depressed quartic, one without a cubic term, and suppose we wanted to express this as the product of two quadratic factors. The factorization must be of the form where our x coefficients are equal and opposite, and comparing the product with the given equation gives us, and since we have three equations and three unknowns, we can find alpha, beta, and u. Now it turns out to be convenient if we find alpha and beta first, because their product is equal to d, which is the constant in our original equation. And so we find, so we know two alpha and two beta, so if we multiply them together we get, and remember the product alpha beta is equal to d, the constant in the original equation. So we can simplify. Note that the unknown is u, while the coefficients are expressions of the coefficients of the original equation. We'll eventually call this a Resolvent equation. What's important in this case is this is an even-degree polynomial with a negative constant, so it must have a real root. And consequently, Euler conclude, real quartics can always be factored into a product of two real quadratics. Now this suggests the next step might be to prove that an eighth-degree polynomial with real coefficients can be factored into a product of fourth-degree polynomials with real coefficients. And Euler does outline those steps. So again we'll begin with a depressed octic, while lacking the seventh-degree term, and we'll assume factors of this form where because their product doesn't have a seventh-degree term, their cubic terms have to be equal and opposite. And we can compare coefficients and obtain a system of seven equations in seven unknowns. Unfortunately, these are non-linear equations, and Euler recognized that it would be very difficult and perhaps impossible to solve the equation by which the unknown u is found. And so we need another approach. And Euler gives it, and we'll talk about that next.


Okay, so we're looking at the samples domain right here. We have the box right from here to there. Right now the box is empty. So why don't we start by throwing in a volume renderer to see what that looks like. And Don, I think you have that set up already. Yep, I did, there we are. Okay, so which part is up and down right now? Okay, I already have it oriented. So this is the bottom, so the lake floor would be down here and the structure grew up from the lake floor. Okay, so that here is the substrate that is growing up this way. And the part that we see down here, this bluish white stuff, that's the lake floor. Yeah, that would be sediment, sand, and microbial mat on the lake floor. And then the microbial processes created this stromatolite. Ah, I see. And it's encased in ice in the sample. And so what we do is this whole area around it, we've shown as transparent because that's the water, the ice. And so we're looking at the mat and the minerals in the mat in the sample here. And then little blobbies like this one here, is that an artifact in the sampling or is it just some other structure that grew in from the side and was cut off in the core sample? That's a little bit of dirt that's caught on the edge of the sample container. So that's an artifact off to the side. And so then that here as well, I guess. Right, so here and here on the side as well. Okay. All right, and how tall is the structure in reality? Give or take? We could do a measurement tool. And I can tell you exactly. So if I put a measurement right at the top, going down to the bottom here. Yeah, I'm going to look over your shoulder a bit. To distance, we are at 21 centimeters. Ah, okay. So I'm going to do a clipping plane that's roughly from your view. And what you see when you cut away part of the sample is you can see that these white areas, they're more dense than the CAT scan. And that's where a calcite formed in the mat itself. So they represent the first part of the growth. These lines represent where the surface of the stromatolite was. And then you can see these laminations going outward that show how the stromatolite grew both outward and upward through time. So this here, that's essentially the skeleton of this growth that we are seeing here, these white parts. Yeah. And so one of the things we're really interested in is how the different shapes that you see, for example, these long, thin areas reflect the microbial growth processes versus the environment changing through time. So essentially the structure that I'm sketching out right here, these little filamental, I see. There's another one right here. Yeah, you did a really good job tracing some of those out. Well, they have very interesting intricate structures. There's another one that kind of bifurcates right here. So I'm going to draw in yellow here. And some of these large ones are connected by some of the smaller ones that you show bending over. We don't know what in the microbial mat makes it grow like that yet or what actually controls where you get the minerals.


Thank you very much. So yeah, this is a joint work with my former and recently graduated student, Vinicent, who unfortunately couldn't make it here to give the talk. So broadly, this work is about designing functions that are moderately hard to compute and in addition to being hard to invert. These are, of course, useful in particular in the context of hashing passwords, but also be used, say, for proofs of work in cryptocurrencies. And moderate hardness here means that on the one hand, the function should be sufficiently expensive to compute, for example, to slow down brute force password cracking attacks, but on the other hand, it should not be excessively expensive to slow down or to affect the performance of a system using it, right? And hence the balancing act. So also these moderate hardness should be somewhat egalitarian. So we will wish that dedicated hardware should not give significant benefit in evaluating the function. And not that this property is really not obvious to enforce, for example, legacy password hashing algorithms do not satisfy it. And so in an effort to satisfy this property, the notion of memory hard functions was proposed. And a memory hard function, or MHF for short, is a moderately hard function that is subject to a time memory trade-off in its evaluation. So what I mean by that is that moderately fast evaluation requires large memory, whereas with small memory, devaluation should be slow. And this should imply a high area time cost, for example, in hardware, which in turn implies a high dollar cost for fabrication of the hardware. And so the goal of memory hardness was present in a number of practical designs for password hashing, including S-Crypt and Argon II, the winner of the password hashing competition. And also there was a substantial effort in the theoretical end to validate the memory hardness of this design, which has led to a large number of papers over the last few years. And if your paper is not up here, it really just because they ran out of space. There are really many papers in this corner. But a common denominator of all of these works, which is the starting point of this work, is that they all treat memory hard functions as modes of operation of some underlying monolithical hash function. As you will see, this is not really an accurate model for reality. But before I get there, just to be a little bit more concrete, let me first introduce the large class of memory hard functions that we will deal with in this work, which is that of data-independent memory hard functions, or IMHFs, which include most designs to date. So an IMHF is defined by a direct acyclic graph, or DAG, G, which is a certain number of vertices, and one of more sources than things. So in this example, it's one each. And the evaluation of the function proceeds by assigning labels to vertices. And in particular, this is implicit here and in the rest of the talk. But the source labels are going to be some value dependent on the actual input to the function. And every other label of a vertex is the output of a so-called labeling function applied to the labels of the predecessors. And finally, the output of the function is the label of the sync. Now, in all of these analysis I mentioned before, the labeling function is a monolithic hash function, which in security proofs is modeled as a random oracle. And so a method theorem here will tell you that if the graph has some good combinatoria property, then the function is memory hard in the random oracle model. So here we want to have a closer look at this assumption that the labeling function is a random oracle. And in particular, if you look at concrete distanciations of memory hard function actual designs, what we see is that the labels are usually very large. So we're talking about thousands of bits. And so it's already very hard to use an off-the-shelf hash function. And plus you might want some more properties that we'll see later. And so what designers do instead is they come up with a dock constructions, I mean, usually based on some underlying primitive. Here it's a permutation. And these designs are usually not random oracles at all, even if you assume that the underlying primitive behaves ideally. So this is an example of a design which is inspired from what Esprit does, but I could have put here the design of Argon2. Jeremiah's going to talk about in the next talk, and it's a similar story. And so we would like to understand what's happening under the hood here. And this was really not done, and it's not clear whether existing analysis apply to actual constructions. So I should mention here that Jeremiah in the next talk is going to talk about some concurrent work that also started looking under the hood and see what happens within actual constructions of labeling functions. Okay, so the main goal of this work was really to initiate the study, and it's really mostly at a theoretical level, as you will see, of the security of constructions of labeling functions and the memory hard functions they're used in when they are built out of simple primitives. And we are going to look at permutations. We also look at constructions from block ciphers and compression functions. Now there are really two main challenges when doing this. The first one is dealing with the primitives themselves. So even if we model them as ideal, so as random permutation or ideal ciphers, as we do in proofs, the techniques that we have so far are really inherently tied to using monolithic random molecules, so we need new proofs. And also understanding what is a good construction of a labeling function is really a hard task to start with. In particular, we don't have any good notions. You might think, for example, that using the notion of indifferentiability that tells us when a construction is a good random molecule in an ideal model would work, but actually memory harness deals with memory-bounded adversaries and the resulting security games are multi-stage games, and we know that indifferentiability doesn't apply to them. So we don't really even know where to start. So in further talk, we'll specifically focus just on the case of permutations. I think it's the most important one. So these are efficiently computable and efficiently invertible keyless permutations. So this is the case as far as I know in most practical designs. And also permutations are attractive, for example, because you might think to instantiate them from fixed key AES and build a memory-hard function out of that. And one attractive feature of that is that your CPU mostly comes with hardware-based efficient implementation of AES, and this will help you already, already the primitive level, reduce the gap between a software and a hardware implementation. Okay, so the general blueprint here is that we start from some DAG, and now we want to build a memory-hard function out of it by instantiating the labeling function from some underlying permutation. And technically, we really have to look at two distinct cases here. The first one is what I refer to as the small block case. This is the case where the labels assigned to the vertices have length which is equal to the input-output length of the underlying permutation. And then another case, which is what most commonly happen in practice, is the white block case where the labels are actually much larger by a multiplicative factor K. And then so we have to build something which will make multiple calls to the underlying permutation. And we'll start with the simpler small block case. Now, a little bit more concretely, the way we will formalize memory-harness in this talk is by looking at the cumulative memory complexity which was introduced by Avan and Serbinenko which looks at the memory usage at every point in time during the evaluation of the function and then sums these memory usages up. Okay, and a little bit more formally, we will consider an adversary that will need to evaluate the function on an input M and its execution will proceed in rounds and at every round, the adversary will make queries to the permutation which we model as a random permutation and in particular, we'll make a vector of parallel queries which might be both forward and backward queries and then produce a state for the next step. And in the next step, the adversary will get the state and the answers to these queries and then these over and over until in the final step when ready, the adversary will output the output of the function on input M. Now, and here the cumulative memory complexity of an execution is just the sum of the sizes of the state. Now, a related quantity is that of the cumulative pebbling complexity of a graph. So here we look at a DAC that might define an MHF and we consider a combinatorial game where the adversary at any point in time can place a pebble on a vertex if all of the predecessor vertices have a pebble on them and also in the same step can remove any of the pebbles. And the goal is to place a pebble on the sink of the graph. So if we look at the strategy, we want to characterize its complexity and what we're gonna do, we're gonna look at the size of the different pebbling configurations. So for example, we might have a strategy or it places an initial pebble on the source. This can be done at any time. It has no predecessors and then might continue by placing another pebble. So now we have two pebbles on the graph. Then we can place one on three but remove one on one. We're gonna have two pebbles and so on. Now we have three pebbles. Now we have three pebbles. Now we only have two. And finally, we place a pebble on the sink. And if we now sum up these numbers, what we get is the cumulative pebbling complexity of this strategy. And now you can naturally have a quantity associated with a DAC, which will be 14 here. We can have a quantity which is the pebbling complexity of the graph which is just the pebbling complexity of the best strategy, the lowest one. And we know by prior works that we can, for example, give graphs or family of graphs with constant degree or even degree two which have optimal CPC, cumulative pebbling complexity of n square over log n. So now intuitively, if you look at the case where the labeling function is a random oracle, the pebbling complexity gives you already measure is correlated directly with the cumulative memory complexity for those strategies that only store exactly labels in memory. However, a general strategy might try to do something clever and compress information on the way stores exers of labels or whatnot. But a result by Alvin and Serbinenko shows that essentially that's the best that it can be done, up to some term that we're actually hiding here for simplicity. Let me, with high probability over the choice of a random oracle, the cumulative memory complexity of an adversary is lower bounded by the pebbling complexity of the graph times the length of the labels which here is the random oracle output length. And so our first result for the small block case will show something analogous for the case of random permutation where we instantiate the small labeling function with a function that simply takes the XOR of two labels. So this is specific for the case of in degree two. And then applies permutation which is invertible. And so to eliminate this invertibility with an XOR, the XOR of the labels to the output again. And the proof of this result follows a similar blueprint as the prior proofs in this domain using something called an expo spark to pebbling argument. But the real challenge which I'm not going to go into due to time is actually to deal with inversion queries in the proof. So the adversary can query the permutation both forward and backward. And this is what makes the proof much harder.  So remember now, our labels are gonna have size K times L where L is the input output length of the permutation. And we want to build a more complex labeling function out of the permutation. And the first question you should actually ask is why should we actually even care? So actually one answer is that this is done in practice but now we actually have a result that shows us a lower bound on the CMC based on the pebbling complexity. So if you want to force a certain lower bound here just choose a sufficiently good graph and large enough to have large pebbling complexity until you meet what you want. But the key point here on one key point that I think also motivated practitioners but I'm not entirely sure is that if we use a permutation our length L the input output length of the permutation might actually be small. So say up to down to 128 bits for AES. And so if we want to achieve a certain large CMC then we need a large graph. And in many cases this also implies a large description of this graph. Some of these graphs are picked by choosing random edges so that will take space. So if we instead we can prove a lower bound that additionally depends on K and in fact we can hope to even have something super linear in K then we might be able to achieve the same lower bound but with a smaller graph description. So it's really about minimizing the description here. Okay, so what we're gonna do here is we're gonna look at wide block labeling functions that are also built out of DAGs. So in particular they say that the label size is K times L and we look at graphs with some in-degree delta. And now we are gonna build our labeling function from a gadget DAG H which is going to have K times delta sources and K designated nodes that are XC nodes and will correspond to the outputs and then we'll actually have constant in-degree two. And then we will compile this into an actual labeling function by using the small block labeling function we defined before. And the advantage of doing this is if now we look at the memory hard function defined by a base graph G with this wide block labeling function this is equivalent to a function defined by a composed graph with the basic graph and the gadget graph for the small block labeling function which we can study with the theorem we had before. And then the goal of course is to find such a gadget graph that will maximize the pebbling complexity. And what is composition here? It's quite natural but just to be precise. So we say we look at our base graph and it has some in-degree say here three and what we do is that first we blow up every vertex by K. So every vertex is mapped to K vertices, so here three. And then for every subgraph which is made by an inner vertex and its predecessors we are going to map it to an instance of the gadget graph in the composed graph. And then we do this of course all over the graph and now we get a new graph which is the composed graph which now has actually also a constant in-degree two. And now we would like to prove something about it and give a concrete instantiation of the gadget graph. So in the paper we give a family of instantiations for gadget graph that we initiate this study and this is an example of such a family. So it has delta times K sources at the bottom, K exit knots at the right hand and the number of vertices here is quadratic in K. So we will expect now to prove something of this form where we'll overbound the public complexity of the composed graph as by the public complexity of the base graph times something which depends on K and delta and ideally should be as large as possible like quadratic in the size of the graph. So unfortunately we don't quite know how to do it. What we do is we do the second best thing we can do and we observe that in almost all cases lower bounds on the public complexity for good graphs are proved via the notion of that robustness. And in particular we say that a graph is D E W robust. If whenever you remove up to E vertices from this deck the graph still has a long path of length at least D in the rest of the graph. And D E W robustness implies in particular that the graph has pebbling complexity at least D times E. And so what we show is that our composed graph as the property that if the base graph is D E W robust then the composed graph has also some good that robustness which is gonna give us a lower bound than from the cumulative pebbling complexity. And the idea of the proof here, I mean I won't read all through all of this but essentially what we wanna do is we wanna map a set of vertices in the composed graph back to a set of vertices in the base graph and use which is not larger than E and then use the existence of a long path in the rest to build a longer path back in the composed graph. And to do this what we need to crucially use as the following property of our gadget graph which is that whenever you now remove from this graph a subset of its vertices which is not too large say at most K over four. This is way larger than K over four but I just did it for dramatization purposes here. Then what we have is that for every possible source of the graph there is always going to be an exit node on the right hand side such that there exists a long path of length omega of delta times K square between the source and the exit nodes or something like this which I have highlighted here. So now what do we get out of this construction? So it's important now to go slowly through the end result. So what we can hope to get here if we get a graph with optimal pebbling complexity say n square over log n and say constant in degree because our final graph we have we'll have roughly n times K square vertices so what we can hope to have is something of the type a CMC which is at least n square times K to the fourth over log n times L. What we get here is something slightly worse namely we get n square times K to the third over log n. Now a question we might ask is this any good? Right, it's not clear what it means here to be good. So this is really a question we haven't asked before. We were just assuming that these random articles were costing you one to call them. No extra memory, nothing. Suddenly we look into it and it's really not clear what we want, okay? So a first thing we might want to do which has been done in several prior works is just to consider the ratio between the efficiency of the sequential strategy that honest user might use to evaluate the efficiency of the sequential strategy that honest users might use to evaluate the function and the CMC lower bound. And if we do that and we look what is the best existing strategy that you will actually implement you get a ratio which is generically log n and for some graphs can even be made constant and this is analog to the best result we had for monolithic random molecules. That's already a good baseline to satisfy but actually this is not really a hard to meet goal you could do it with much simpler constructions. So a better thing we might want to look at is how high the CMC is as a function of the running time of the sequential strategy. So you're given a certain budget of time to hash passwords and you want to get CMC which is as high as possible. And here what we see is that if we've right now the running time is n the number of vertices times the time to evaluate the labeling function we just built which is roughly k square. And then we compare it with our CMC lower bound then the ratio is roughly sort of the order n times square root of t lambda times l. And again ideally we will hope to achieve n times t lambda times l but I'm really not sure whether this is feasible with a simple construction. So it's a great open question but it seems it's at least as hard as again looking inside your labeling function and making it again a memory hard function plus more. So this might not be exactly what you're looking for. And another interesting question here is that we had this quadratic blow up from k to k square complexity. Maybe we can build something which is linear although the running times and what does it even mean? Okay but it's important here that we are doing something non-trivial. If for example you use something like based on Merkel-Darmgart to instantiate your labeling function and then you try to prove something about it but you will have gotten there was a ratio of n times l that will not depend on the actual time to evaluate your Merkel-Darmgart. So we are achieving something far from three by using this extra term in the ratio so the ratio goes up. Okay so as you see I think in this work we open more question that we actually solved. So this is really mostly the goal was to prove some basic theorems and to attract the attention to the problem and start giving you some theoretical designs. But I think it's a very important question and something that has been overlooked just because it's not easy. In prior works on memory hard functions but there are a lot of problems. I mean one open problem. So one of them is of course that I really consider only CMC. There is a lot of work on considering other metric and other aspect like bandwidth harness, like space sustained space complexity and much more. So we would like to extend it to that. Also it would be like one would of course like to look at practical design and prove something about them whether they're secure or not and we actually don't really have a good sense. Many of them do not fit in the framework that I used there in a graph theoretic framework. Also exactly for that reason we would like to have some more generic high level properties of these labeling functions that are sufficient. And also at the end we would really want to understand all of these parameter trade-offs and what they really mean. So if I make this labeling function even slower and so on what do I really want and how easy it is to represent these graphs and much more. All right so thank you. So this ends my talk. So we are kind of working on an extended full version of the paper that hopefully is going to appear soon where we want to highlight a bit better these open questions and parameter issues and so on. So I'm happy to answer any questions. Thank you Stefano. If anyone has a question please come to the microphones at the front. Long walk. Long path. Let's have a quick easy question. So I'm just curious why the cumulative memory complexity is the right measure rather than the maximum for example? Oh I see so yeah so the point is that you want to differentiate between strategies that for example require a lot of memory just because there is a peak where you're using memory just at one point and then you don't need it. So what it becomes important is for example when you do mass evaluation and you want to evaluate multiple instances then you could use that free memory for. Let's thank Stefano again.


So, this is my second lecture on generalised linear models. And here is the content of this module, the exponential family of distributions fitting generalised linear models and logistic regression models. So, in simple linear regression or in multiple linear regression model we make several assumption on error term like you know the error term has mean zero variance sigma square and they are uncorrelated. And also we assume that epsilon or they follow normal distribution with mean zero and variance sigma square. Now in a topic called transformation and waiting to correct model in adequacy, we have learnt you know how to deal with the situation when the assumption on constant variance and also that uncorrelated assumption is violated. And what we learnt in this topic is that how to deal with the situation when the normality assumption is violated that means the response variable or the error term they follow some other distribution not the normal distribution. So as I told in the previous class you know this generalised linear model analysis comes into play when the error distribution is not normal. So, the error distribution is not normal means the distribution of response variable is also not normal, but let me clear the fact that the error distribution is not normal, but the error distribution or which is same as the response variable distribution must be member of exponential family. So this should be clear like you know this generalised linear model is applied when the error distribution is not normal, but the error distribution must follow a distribution from the exponential family like we learnt in the previous class that like normal distribution of course it is follow it falls in exponential family and then binomial Poisson and then gamma exponential negative binomial they are in all in exponential family. So what I will do I sort of repeat this fitting generalised linear model again because this is very important fitting generalised linear models. Suppose you have a set of independent observations suppose we have set of independent observations the observations are y i this is the response variable and x i prime. So this is a vector and suppose it has p components associated with p regressor variables. So these are the observation we have and we have n observations i is from 1 to n and as I told this x i prime this is x i 1 x i 2 up to x i p and here this response variable is not from normal. So this is from some exponential type distribution of canonical form. So we know when a distribution is of exponential type and then the joint pdf probability density function is so f y 1 y 2 y n theta and phi which is basically product of this marginal pdf. So the marginal pdf is it is exponential type so the pdf is of this form exponential y i beta sorry b theta i plus c theta i plus d y i and since this distribution is of the canonical form that is why ay is equal to y and this is the product of marginal so i is from 1 to n because the observations are independent that is why you can find the joint pdf just by multiplying the marginal pdfs and this can be written as as I wrote in the previous class this is exponential some i is from 1 to n y i b theta i plus c theta i i is from 1 to n plus sum over i equal to 1 to n d y i and here this theta is a vector of parameters of interest and here the parameter of interest is theta and this vector is say theta 1 theta 2 theta n. So the ith observation is coming from an exponential type distribution with parameter theta i and this phi is vector of nuisance parameter. So next what we want is that we are given say y i and x i like the previous cases. So we are given the response variable and we are given the value for the p regressors and what you want is that we want to explain the variability in y in terms of x i's but the only problem here is that if the y i is from the normal distribution then we know how to fit a model between y i and x i but here the only problem is that this y the distribution of the response variable does not follow normal distribution here and then how to how to fit a appropriate model between between the response variable and the regressor variable. So that is the main objective here. So what we expect is that we would hope that the variation in y i or say expectation of y i that is nothing but theta i and this all this theta i theta 1 theta 2 theta n they can all be different. So the variance in y i or theta i values could be explained in terms of the x i's values and we would hope that we could find a suitable link function suitable link function say G of theta i such that the model is the model G theta i is equal to x i prime beta this held. Let me just complete my writing here and then I will try to explain this part little bit. So where beta is the regression coefficients beta is beta 1 beta 2 beta 1 beta 2 beta 2 beta 2 beta 2 beta 1 beta 2 beta 2 beta 2 beta 2 beta 1 beta p is vector of regression coefficients and this link function this link function is often the natural parameter. I am sure that you may face problem here understanding this part but let me try to explain this one. So in usual case when y i is from normal distribution what the model we fit is that we fit the model y i is equal to x i prime beta plus epsilon. So this is the simple or multiple linear regression model and then this can be I can write this model as say expectation of y i equal to x i prime beta because of the fact that expectation of epsilon is equal to 0. So my model I can also write this model as theta i is equal to x i prime beta. So this is the model in case of so the model finally the model is theta i is equal to x i prime beta when the response variable is from the normal distribution. Now I hope you can recall that the natural parameter for x i prime beta is x i prime you know normal distribution is also in the exponential family and the natural parameter for normal distribution is the natural parameter parameter is theta if theta suppose theta is the mean. I mean the link function is associated with this natural parameter. So the link function here the function I am talking about this is g theta. So g theta is equal to the natural parameter theta. So when it is normal my g theta is equal to theta so that is why I fit the model theta equal to x i prime beta. Now in the other case this suppose y is not from y does not follow normal distribution it follows some other distribution from the exponential family say binomial then my natural parameter for binomial is ln theta i by 1 minus theta i. So this theta is the probability of success in i ethereal. So this is the natural parameter for the binomial case binomial with parameter n theta i. So this is the natural parameter in that case in case of binomial.  So in case of binomial we will go for the model that we will go for the model of g theta i is equal to x i prime beta. And we know that in case of binomial this g theta is equal to ln theta by 1 minus theta sorry i equal to x i prime beta. So this is the here you can write this in the compact form also may be finally the model is theta i you can write it as theta i is equal to exponential x i prime beta plus 1 plus 1 plus 1 plus 1 exponential x i prime beta. So this is the model in case of binomial and this is nothing but this is nothing but the final model is expectation of y i is equal to expectation sorry exponential x i prime beta by 1 plus exponential beta. So this is the model we have to so that means the y equal to this plus epsilon is the model in case of the response variable follows binomial distribution. So I will talk about this case right now in detail right. So let me consider this fitting generalized linear model in case of binomial distribution binomial distribution. So let me write it clearly suppose we have data we have data say y i x i prime from a binomial distribution from a binomial distribution with parameter say binomial n i let me write it p i instead of theta i am writing p i. So p i is the parameter of interest and n i's are nuisance parameter. So I have a set of observation from binomial distribution then how to how to fit a model I have already talked about this one but I will write it very clearly here. Now let me write it as p i instead of theta i am writing p i. So p i is the parameter of interest and n i's are nuisance parameter. So I have a set of observation from binomial distribution then how to how to fit a model I have already talked about this one but I will write it very clearly here. Now this y i the single observation y i is of the form r i by n i where r i is the number of successes in n i trials. So r i is the number of successes in n i trials. So you know binomial distribution and here y i is not really number of successes in n i trials it says proportion of success basically each having probability p i of success and this x i prime this one is basically x i 1, x i 2, x i p is a set of observations of p regressors associated with y i and this x i prime this one is basically x i 1, x i 2, x i p is a set of observations of p regressors associated with y i and this we know that this binomial I will also give an example to illustrate this part this binomial distribution is a member of the exponential family. So what I will do first is that I will write down the joint p d f because that well from that one we get the natural parameter and also the link function. So the joint p d f of y 1, y 2, y n joint p d f let me write it as f of y 1, y 2, y n is equal to product of y 1, y 2, y n is equal to product of y of n i y i n i c y i p i to the power of y i into 1 minus p i to the power of n i minus y i from i equal to 1 to n and this can be written as i equal to 1 to n and this can be written as 1 to n exponential say y i l n p i 1 minus p i it is not difficult to verify this plus n i l n 1 minus p i plus l n n i c y i and then you can finally, write it as exponential just sum over i equal to 1 to n y i l n p i 1 minus p i plus sum n i l n 1 minus p i i equal to 1 to n plus i equal to 1 to n n i c y i plus sum i equal to 1 to n c y i l n. So, this is the joint p d f of the observations we have which are from the binomial distribution. Now, the same thing what we want we have we are given y i and x i prime and we would try to explain the variability in y in terms of x i. So, same thing so we would hope that the variation in the in the response variable y i or in expectation of y i. So, here you know this generally it is n p, but here we are assuming again this y i the observation is number of successes by n i. So, this one is basically p i. So, the variation in y i or in p i could be could be explained in terms of of the x i's values. That is we would hope that we could find a suitable link function link function. So, here the function g such that this g of p i is equal to x i prime beta and this link function is equal obtained from the natural parameter. And for binomial distribution the natural parameter is so the natural parameter here meter is l n p i 1 minus p i. So, this is basically g of p i. So, that is why we fit the model and we have to find a suitable link function we fit the model l n p i by 1 minus p i equal to x i prime beta. So, this one is nothing but beta 1 x i 1 plus beta 2 x i 2 plus beta p x i p. And this is the result of the this one can be written as finally, that p i is equal to exponential as I wrote before also exponential x i prime beta by 1 plus exponential x i prime beta. So, this is same as writing expectation of y i is equal to this and which is equivalent to say that y i is equal to this plus epsilon. So, this is the model you know instead of fitting y i equal to x i prime beta which is the case for normal distribution we are fitting the model like y i is equal to this expression plus epsilon. And then expectation of y i is this one. So, the model we got is finally, is p i is equal to exponential x i prime beta by 1 plus exponential x i prime beta right. Now, when x i prime beta is equal to epsilon is equal to beta 1 plus beta 2 x i 2. That means, only one regressor and the other one is of course, the dummy variable you can put x 1 x i 1 which is a dummy variable which is one for all observations. When this is true let me call it star when this is the situation then star is called logistic function. So, we have the model with us now this is the model we have to fit when the response variable is binomial. So, the model we got is that expectation of y i or which is equal to p i is equal to exponential x i prime beta by 1 plus x i prime beta sorry exponential. So, we have the model and then how do we fit the model fitting the model means here the regression coefficients are beta 1 beta 2 beta p. So, we have to estimate those things. So, here we will use maximum likelihood method to fit them. So, estimation via maximum likelihood. So, to estimate beta we use the method of maximum likelihood. So, first what we do is that we construct the likelihood function or compute the likelihood function l which is the likelihood function which is nothing but the joint probability of y 1 y 2 y n and we know that this is exponential just now we computed this is y i l n p i 1 minus p i plus sum over i equal to 1 to n i equal to 1 to n n i l n 1 minus p i plus l n n i c y i right. So, this is the likelihood function. So, this is the likelihood function and then it is convenient to work with log likelihood. Log likelihood is nothing but you know log of the likelihood function l n. So, this simply it is become sum over y i l n p i 1 minus p i i is equal to 1 to 1. So, this is the log likelihood plus n i l n 1 minus p i i is from 1 to n plus l n n i c y i. So, this is the log likelihood and now what I want to do is that see ultimately we have to estimate the parameter beta and the model we have to sorry we have to fit this model we have to fit the model p i is equal to exponential x i prime beta by 1 plus exponential x i prime beta and then what we will do is that we will just write this log likelihood in terms of beta. So, you can check that this one is x prime beta right. So, this one is summation y i x i prime beta because this l n p i by 1 minus p i equal to x prime beta from there only we get this one plus i is from 1 to n you can check that this can be replaced by you have to put minus here by n i l n 1 plus exponential x i prime beta it is not difficult to check this one. So, from here you can check that this l n 1 minus p i is equal to this plus so l n n i c y i. So, we have the likelihood function or log likelihood function in terms of beta now. So, how do we estimate beta? You maximize log likelihood l n with respect to beta that means so here you know this beta is a vector and it has p components beta 1 beta 2 beta p. So, you have the log likelihood involving beta now you differentiate this log likelihood with respect to beta 1 beta 2 and beta p. So, then you will get p equations and you have p unknown and then you can solve for p 1 p 2 and sorry you can solve for beta 1 beta 2 and beta p. So, this is how you know you have to find the estimates of regression coefficients beta and it is not so easy to do this for a given problem. So, may be you know this numerical search method or something called iteratively reweighted least square this is i r l s could be the result of the used to compute maximum likelihood estimates of. So, now again you know to explain this example of binomial distribution you considered.  So, here we have data called a pneumoconiosis data and this is you know this is lung disease this pneumoconiosis is a lung disease that results from breathing in dust in coal mines and here you have the data like number of years of exposure and the data can be read in this way. So, number of years of exposure is say 5.8 years and total number of minor is 98. So, this many workers number of severe cases is 0. So, if I mean then the proportion of severe cases. So, why is the proportion of severe cases and that is 0. So, 0 by 98 is 0. So, the number of years of exposure if it is 5.8 or say 6 years and here then the probability that somebody will be severely effected by this pneumoconiosis is like 0. Similarly, if you see that the number of years of exposure is more then there are chances of severely effected by this disease and here you can see if somebody is exposed for say almost like 50 years then it is almost the probability is half that a person will be effected by this disease. So, I am sure that you know understood the problem here. So, I have the data now let me write in terms of my requirement like so, I have the response variable y i. So, y i is the proportion of minors who have severe symptoms. So, this proportion these are the proportion and I want to see whether the variation in this proportion can be explained in terms of the number of years of exposure and that is my x i. So, here I talked about this x i vector. So, this vector is consist of only one component it is a simple regression model type of things. So, I have this data for i equal to 1 2 3 4 5 6 7 8 for i equal to 1 2 8. So, my y 8 is the proportion of equal to 0.45 and my x 8 is 51.5 years. So, what I want is that I want to see whether the variation in y i or in the proportion of severe cases can be explained in terms of the number of exposures. But the problem here is that this y i is not from the normal distribution. So, it is sort of binomial if you I mean this number is of course binomial number of severe cases is binomial. So, here the probability distribution for the number of severe cases is binomial. So, we will fit a logistic model and there are only one regressor. So, we will fit a logistic regression model. So, this is a logistic regression model. To the data and my model is like y i is equal to exponential x i prime beta by 1 plus exponential x i prime beta and I should write this expectation of y i which is equal to p i basically. And here you must have observed this x i prime beta is equal to beta 1 plus beta 2 x because there is only one regressor right that is the number of years of exposure right and then you go for. So, you have the model and then you know how to fit this model using maximum likelihood estimator. And finally, you can check that the fitted model is y i hat which is equal to exponential 4.79 minus 0.0935 x. So, this is the number of years of exposure x that means I am writing this beta 1 is this and beta 2 is this plus 1 plus exponential 4.79 minus 0.0935 x. So, this is the binomial I gave an example to illustrate the binomial case. Let me go for the Poisson distribution now. So, suppose we have data y i x i prime from Poisson p with parameter say mu i that means expectation of y i is equal to mu i. Generally, we write lambda i is ok. So, you know that this distribution also is in the exponential family and the probability mass function f y mu can be written as exponential y l n mu minus mu minus l n y factorial. And here the natural parameter is l n mu is the natural parameter. I am specific about this because this from this natural parameter will get the link function.  So, again what we are suppose we are given y i and x i and we want to explain the variability in the response variable y i in terms of x i. So, the variation in y i could be explained in terms of in terms of the x i's values. And the model we fit is that we fit the model the model is like g mu i is equal to x i prime beta. And we know that this link function is equal to l n mu. So, the model we fit is so l n mu i is equal to x i prime beta which is equal to beta 1 x i 1 plus beta 2 x i 2 plus beta p x i p. And finally, you can write this as mu i is equal to exponential x i prime beta. So, this is the final model you have to fit and this is nothing but expectation of y i is equal to e to the power of x i prime beta. So, this is same as writing that y i. So, you have to fit the model y i equal to e to the power of x i prime beta plus epsilon. So, whereas for the normal case it is y i equal to x i prime plus beta because for the normal case your g mu is equal to mu that is why. Now, what I will do is that I will talk about some reasonable choices of link function. Suppose of choice of this link function because the model is model depends on this choice of link function. Suppose the distribution is normal distribution and the link function. So, in case of normal you see I am just bringing one slide from the previous lecture. Here you can see the natural parameter is mu you can forget this sigma square because this is nuisance parameter. So, you can write it simply mu. So, then the link function will be g mu equal to mu. The link function will be g mu equal to mu and this is called the identity link. So, in case of binomial we know we have just established the model. So, g mu g p let me write p p is the probability of success in one trial this is l n p by 1 minus p this is called logistic link these are the name and then partial my g mu is equal to mu. This is called log link and for exponential my g mu is equal to 1 my mu and this is called reciprocal link and of course, for the gamma distribution is same because exponential is particular case of gamma distribution it is g mu equal to 1 by mu which is also called reciprocal link. So, in this module we have learnt if the error distribution or the distribution of the response variable is not normal, but it is from some exponential family the distribution is from some exponential family then how to deal with the situation how to fit a model. So, we have to stop now that is all thank you.


Our next caller is Teresa from Iowa. Hey, Teresa. How you doing? I'm well, Sal. Hello. Hi. What's your question? How can we help you? I'm just starting six months into weightlifting. And I have bicep tendon tear on a bench press. Really, really don't want to stop. So I'm kind of discouraged. What can I do? I can still do my squat and my deadlift. What can I do for my biceps and my chest and stuff as I wait to recover? Right. That's a great question. First, I need to know how you tore it. What did you do? Well, I'm new to this game, so I'm just going to say I was doing a primer exercise. And I can't remember the name, but I was laying down and I was putting my hands up, squeezing shoulders, and then putting my thumbs to the ceiling. It was hurting a little bit, and then I went right into a bench press, and I racked myself up. Pro and Cobra? Is that what you do? Or handcuff with rotation? Yeah, one of those two, it sounds like.  Okay, good. That's a good, and I'm asking that question because sometimes it's just inflammation. Right, a bad strain. Yeah, or a strain. Where do you feel the pain? Right in the front of my bicep, up towards my shoulder. Oh, wow. Interesting. You know, I just talked to somebody. I know we just got off the phone off of something similar. Yeah, very similar pain. Does the pain get better when you stretch the bicep or does it get worse? It is getting better. It's been four weeks now, and I've been just primers, and maybe some curled, but it definitely gets better as I stretch, and I'm thinking the answer is patience, but you know, I'm just new on this, and I'm really wanting to be done. How? Yeah, no, no. I feel you getting injured is probably, it's got to be the most frustrating, especially if you're motivated and you're really getting into it.  So here's some good news. Training other parts of your body does have a systemic effect on the whole body. So to give you an example, there's these very interesting studies where they'll have people work out just one arm, and what they'll find is there's some strength gains even in the other arm that's not being exercised.  Not to mention your leg work is where you're getting the biggest bang for the buck anyways. When you talk about calorie burn and staying fit and building your metabolism, luckily the bicep is going to be something that's very small as far as contributing to that. Totally. Now here's the second piece of advice I'll give you is do deep tissue massage on the affected bicep and do light static stretches. When you do your primers, don't be so intense with them right now. Go through them easy. Don't be so intense. Static stretching and massaging of the bicep might help. And then here's the other thing. When you get back into pressing, make sure your chest is out and your shoulders are pinned down and back. When the shoulders roll forward, it places a lot of stress on the bicep tendon for a lot of people. Okay, great. Thank you. Yeah, no problem. Thank you all. Yeah, no problem. So what are your goals by the way? You started six months ago. What are your overall goals? I to lose all of my weight and actually to be strong and pushing 60, I got 30 years left of me. I know it and I'm not going to be able to do that if I continue down the path I was, I became sick. So I want to get strong. I love that you're starting right now. What was it six months ago that got you going and how long have you been listening to Mind Pump? I've been listening to Mind Pump probably right before I started. I became very sick and I said I had a toxic work environment so I retired early and said it's my time. Good for you. Good job. Very, very good. Okay, go ahead. Continue. Sorry.  I just appreciate everything. Awesome. The way you intrinsically take this approach, it's not about just gaining muscle. It's about whole body health and that's really what I want. Beautiful. But when clients would come to me and talk the way you're talking, I would just get so excited because you've taken the hardest steps. It's hard. It's actually difficult to get people to get them where you're at right now. So that's excellent.  Are you staying hydrated and avoiding inflammatory foods? Yeah. Are you eating enough fish and are you getting good Omega 3 fatty acids in your diet? Yes. Yeah, I've started. I wasn't before. I'm taking some joint supplements from Legion because of knees and I am drinking water. I always have been a big water drinker. So the diet is going very well. I'm down 40 pounds. Wow. Wow. Congrats. Oh, yeah. I feel good, but I want to get better. Okay. I want to be stronger.  That's phenomenal already. Yeah.   I want you to take this to heart. Okay, Teresa? Okay. Okay. Take your time. Don't rush anything. Take your time. Allow your body. Give your body a chance to adapt and to improve. If you take this approach, you'll get there faster than if you allow this feeling that you have, which is contagious. I can hear it over the phone right now.  That feeling that you have, if that takes over, you're going to push yourself a little harder and do a little more than is necessary. Take your time. Be gentle with yourself. You'll get there. You will get there.  So be gentle with yourself. You'll get there. You have the right attitude. Yeah. Especially slow those reps down. Yeah. Take your time with that and find your thresholds. And also, you know, that strength is going to come back. You just got to trust the process. And when things like this happen, because this is common even for us, as long as we've been training, I still get nagging pain and injuries that come out of nowhere because I've pushed myself too hard or I overreach. You know, this is where I shift my focus. Okay. You know what? I need to be doing more mobility or yoga stretching or more walking. So when these things do happen, because sometimes they're inevitable, they'll occur during your journey. Don't let it discourage you and get frustrated and set back or don't let it try and push through it. You know, shift your focus a little bit and say, Hey, you know what, I'm going to stay dialed in on the nutrition, get some more walking in, maybe work some mobility, let my body recover a little bit. And then always remember, I would say this in the podcast all the time, we are always trying to do as little as possible to elicit the most amount of change. And when we're motivated, it's really easy to want to keep overreaching and stretching and pushing ourselves. But really the goal is just to do a tiny bit more than what you were doing the month before. So trust the process. That's right. You're doing great right now. You're doing very good, Teresa. I appreciate you listening to the podcast and thank you very much for your question and congratulations on the 40 pounds weight loss. That's phenomenal. That's right.  Well, you know, we didn't ask you if you have any of the programs. Are you following any of the maps programs right now? I got maps prime after the injury. The reason I got to be honest, I went to maps of anabolic and I got overwhelmed by some of the exercises that I don't really know the correct form because I'm all about form instead of gaining more knowledge on different lifts. Teresa, I think you should start with map starter. I think that's going to be the better. Doug, set her up, huh? We're going to give you map starter. Start there. Follow map starter. Thank you. When you're finished with map starter, once you complete map starter, go back to maps on a balik and then do pre-phase. Do pre-phase for about five to six weeks, then move into phase one. Okay. Okay. Okay. That sounds great. Beautiful. All right. Perfect. Thanks again, Teresa. Hey, Merry Christmas, guys. Hey, Merry Christmas. Back-to-back bicep issues. Yes, but so here's the beauty of this. Right. Same question? Different. Totally different. Totally different prescription. Yeah. So she's got the right attitude. That's so hard to teach people. I can tell the right away. I mean, she's only been going six weeks, right? Six weeks, 40 pounds already. She's definitely the type that is pushing, right? She's not, she's ambitious right now.  I think she said six months, if I'm not mistaken. Six months. Even six months. 40 pounds in six months is great. That's a lot. Yeah. You can hear like she made that, and I've had a few clients like this, right? They get that switch. That switch turns, and it's like, it's real. It's not fake, but the challenge with it is overdoing it. It's laser focused. I love training someone like this because they, like you, and you did a good job sell of complimenting her that she did the hardest part. Like she's gotten started. She's motivated. She wants to make a change. She has the right attitude of a holistic approach. She's just over ambitious.  You're doing great. And just reminding them, you're doing phenomenal. Yeah. Yeah. And to listen to your body a little bit. And that's exactly. Yeah. More isn't always the answer. Right. And that's hard to take when everything's kind of working. I would much rather pull the client back than have to push them. You know what I mean? Yeah. Yeah. No. 100%.


As you know, we have asked you to watch the tutorials beforehand and read up on some training materials beforehand. And then we've organized this session especially for you to ask questions about either tutorials or the training materials. I noticed that with us is apart from Emily Hermanns, who will be presenting. So it's not Francoise van Doorn, but she is on a different computer. But Emily Hermanns, who is presenting, and I've seen that Sarah Jones from GCC has joined us as well. So that means that at least two experts of Openair and Foster are here to answer all of your questions. A little bit of housekeeping is, first of all, the session is being recorded. So we will send you the recording afterwards. You can ask your questions either via our Mentimeter presentation, which means that you should go to menti.com and use the code 1787158. So you can just type in your question there, but of course we'll also be looking at the chat. So you can also just type in your question in the chat box of this webinar and then we'll answer them in chronological order as they come. I'm muting you all, so please don't unmute yourself because I think we're a bit too many to allow for people to just talk at their own judging. But if you really think it's better to talk, just ping me in chat and we can unmute you so that we can have a real conversation. In any case, Emily, I would say the floor is yours. If people have questions, please start typing them in chat or in the menti.com. Okay, thank you, Grant. So this is a Q&A session and questions and answers sessions will not be presenting. So there was one question already about the new framework program of the EC. And the question was, what do we know already about it? Is there already some news about policy? At this stage it's a little early to say. The new program will start in January 2021, so that's two years ago, two years away. There has been a press release and what it say in regards to Open Access and Open Science is that it will be, Open Science will be the default option. So they will continue the way that the program has been going now. So Open Access will be the default for Open Access to publication and also for data to allow innovation and spreading of results. So we suspect that there will be a continuation of the current policy, but it's too early to say and we'll have more news in 2019 probably. If you want to keep up to date you can always subscribe to the open air newsletter. We always report on new policies. Grant, I think my screen is shared at the moment. Shall I open the Mentimeter questions? No, your screen is not shared at this point. So I hope that answered the question about the next framework program. Do you know if there are more questions? I don't see the Mentimeter at the moment. If you want to, I can share my screen for now. It's fine, but I have Mentimeter open on my screen, but maybe if people have questions they can also just ask them in chat. Mentimeter is mainly useful for asking questions in advance. So are there any questions? You're all very quiet in the chat box. Maybe it's helpful to get a sense of the audience. Most of you are supporting open access and open data. Do you get inquiries regularly from researchers about these things? I do recognise some names in the audience, but some people I don't know. It would be useful if you could share with us what your experiences are with disseminating news about policies. Claire, Austin from Environment Canada. It's good that we've got global reach, not just European reach, and they support open science. Claire, do you get any questions about Horizon 2020 policies? One thing just while people are typing in questions or comments on what they do. Just to follow on from what Emily mentioned earlier on about the next framework programme and how Horizon 2020 policies will potentially change. Gwen and I were doing some training workshops for Foster the other week, helping EC project officers on how to review data management plans. And one of the comments that was made to them in some internal training was that it's likely a DMP will be compulsory in future. So regardless of whether people are sharing data or not, they think the DMP will be needed to address the data management. And I think if that does transpire into policy, I think that would be a good step. I think the DMP can be quite useful to the project itself. Sarah, do you expect that there will be any more guidance about DMPs from the European Commission? Or will there be something that will be left to the national level? I think the EC potentially will have a bit more guidance. I think it'd be useful if they shared guidance on how they're reviewing the DMPs. That's something we've recommended in a report that OpenAir did with the Fair Data Expert Group. They have an internal framework which isn't public, but it's based on their own template, so essentially you will know what the coverage is. But I think if they explained a bit more about their internal processes and maybe gave some more discipline specific guidance, that would be useful. I know they're keen to have more discipline specific guidance, but I think they may try and collaborate with others, like the Science Europe Consortium or maybe some of the S-Frees. So it's not necessarily the Commission releasing this, but they hopefully support it being developed elsewhere. Okay, thanks. I was going to ask a question to the audience. Do you find one aspect of the EC's policy easier to deal with, like the open access side? Is that easier than the open data? Are there particular challenges that you face? Okay, so there's a question whether there are any guidelines regarding human data. I think I'm not sure, Sarah, you can go into that in detail. I do know that on Thursday we also have a webinar posted by, well, given by Ches and Jesses, who will deal with the legal and ethical aspects of sharing data and the social sciences. But Sarah, maybe you can tell, you know a bit more about that and share right now. The EC policy is quite generic. There's kind of two main sets of guidelines around the data side. There's the guidelines on fair data management in Horizon 2020. And then there's also an overarching open access and open data guide, which covers both areas. And I think because the Commission funds every type of research, it doesn't go into lots of detail on how you handle human data. But one of the things that do stress with their open by default policy is that that's meaning that you should be as open as possible. So where there are sensitivities around the data or a need to share under restrictions or through data sharing agreements, that's completely permissible. That's not an issue. It just needs to be outlined in the DMP, what the plan is. In terms of handling human data, I think the more comprehensive guidelines come from actual data services. So I know in the UK, the UK data service, the UKDA, they have a lot of really comprehensive guidelines on negotiating consent and anonymizing data and using different types of services like secure data services. So their guidelines would be a lot more specific than the ECs, which is probably a better kind of call if you're looking for more practical advice. You can paste a link to that website in the chat. Okay, Sarah said there was another message, but I'm not sure if you've seen it because it was sent in private to organisers. It's by Erin Fennerty and she says the following. Our team is researching... Oh, maybe Emily, if you're okay, I will just copy the question into the chat so that everybody can read it. Okay. Erin, sorry. Yeah, so I think it's related to the question, previous question as well. So it says our team is researching young mental health and I think there are concerns that sharing data, that sharing of this information is too sensitive in nature to have full open access to research data. Yes, ours are keen to comply with open data, but do you have any advice for sharing of sensitive information or what the policy is regarding data concerning minors? Our team is researching youth mental health and I think there are concerns. Okay. Okay. Yeah. So again, I think the thing that I would stress is that the EC's policy, although it's open by default, that means that you make data open wherever you can. So it doesn't mean that everything has to be open. It might be that within your project, there are certain data sets or certain elements of data that you can make openly available and then others where, you know, there's a risk of disclosure or there's sensitivities because you're working with minors or potentially you haven't got consent to share that data. So I think the key thing where you're working with, you know, more complex or challenging data sets in terms of sharing, is first off to see what consent you can get. So to ask whether the participants will allow you to share the data, to reuse it in other studies, obviously to anonymize the data if you are going to be sharing it. And if anonymizing the data would, you know, essentially strip away too much of the valuable information that would allow you to reuse it and to make the data meaningful, then maybe you have much more restrictive sharing. So you have data sharing agreements that either constrain who can use the data or for what purpose. Now that's not open data sharing, but it's still sharing in some form and that's better than no sharing at all. And I think the other thing that for the EC's policy, because they talk about open data, but they're also really now emphasizing fair data. I think it's really valuable to make sure there's metadata online, even if the data themselves can't be accessible to all, there should still be a metadata record. So people know about the research you've done and they know there's some data there and potentially they have to get in touch with the investigators and agree to a data sharing agreement to be able to reuse it, but there's still a big value in knowing that that data exists. So yeah, I think the main thing I'd stress about the data policy is that it's a scale. It doesn't mean that everything has to be open. There's different shades of gray and it's for every project to apply that policy as applicable for their data. Okay, thank you very much Sarah. I'm just sharing my screen just to show you that we will have later this week, we will have some other webinars related to both research data, management in general and fair data and also legal and ethical considerations for sharing research data. So if you're more interested in this topic, I would suggest that you also attend these webinars and definitely watch the tutorials and read the training materials. In the meantime there are some questions from the Mentimeter. So the first one is a bit of a philosophical one. So do you think the EC is actually making a difference in open access and open science? Yes, I think if I speak from my experience, I work at the university in library. I can say that policy definitely has an impact on awareness. So as you know, libraries are most often the ones paying for subscriptions and things like that. So that means that the cost of publishing is often a little bit hidden from researchers. And now it's a whole movement of open access and also definitely open data. Researchers are more and more aware of how the system works and why it's important that science needs to be open. And I think the EC has made a lot of progress possible for the open science movement in terms of awareness and in terms of trying to be a force that moves forward in the sense it's no longer just the libraries or just a small group of open science advocates who are behind this policy. More and more national policies, more and more university policies adapt to the open science policies of the EC and I think in that way the EC is actually making a difference in open science. I don't know if you want to supplement Quinn or... Well, I think first of all the budget is quite significant. So the EC is I think worldwide one of the greatest funders. I'm not sure about the exact amount of it but I think Horizon 2020 was over 80 billion euros. So in any case this is... I mean the mandate is quite like a strong one in regarding the number, the sheer number of projects and publication. It's actually influences. So I do think that they make a difference and on a more philosophical level I think the type of policy that the EC has been establishing for quite a while especially in the beginning days was where it's a focus on depositing in a repository and making it open as soon as possible. I think this was the fact that the EC adopted this type of mandate has inspired a lot of local funders and institutions to do the same. I see that somebody in the chat also said that they believe there's a difference in open data and open science. So Claire maybe if you have some more context on your statement that will be nice to share. Yeah, I think there's been a huge shift with the EC policy when you look at the different institutions across Europe and how many local policies or national funder policies are emerging kind of following on from the EC stance and also just the investment that member states are making as well. So individual universities having local support teams I think that's really shifted over the last few years and I think it's in light of European Commission because people realise that their researchers and applicants need support. Like Gwen says they provide a significant amount of funding to research but also they fund a lot of infrastructure projects you know projects like Open Earth and that similarly makes a big difference. Hi, it's Claire here. Thank you for doing this and for welcoming people from across the pond. Yeah, I do think that the EC European Commission is making a difference. I've been reading many of the documents directives and so on that have come out especially over the last six months. So we have the EC's Open Science Policy platform recommendations, the European Open Science Cloud and then various countries have been putting forth national plans. France for example, the national plan on Open Science as part of the Open Government partnership. So these are from my perspective are very forward looking and helped our influential in moving the Open Science forward globally I believe. Thank you. Okay, thank you very much Claire. That's very useful. There is one other question in the Mentimeter unless somebody has something to add, Sarah or Emily? Nothing specifically, I think it's good that it shows the impact of Europe that people from across the pond are coming to these webinars because they see it as an influential thing to track the developments of. So it's nice to see that kind of global transition. Yeah, indeed. Another question is and this is something that I will answer very quickly and it does open air plan to support a new round of post grant APC. In short, no. And it's mainly for those of you who are not aware Open Air supported a fund that covered open access fees related to FP7 projects and that was in the previous round of a version of Open Air. So this is not something that we continue to do and the main reason for that is that actually open access fees are an eligible costs in Horizon 2020 and project writing. So you're supposed, if you submit a proposal, you're supposed to calculate the potential costs of open access fees and just include them in your proposal. So there's no real need for a separate fund covering this. And maybe I can just quickly go to the next question as well and those questions have been asked via the Mentimeter. So what if I can't afford open access fees and I just, I think this is an important question but maybe Emily, you can elaborate a little bit on what people should do if they don't have the budget to pay and if they write it into their project proposal. Yes, maybe I'll quickly share my screen. Yeah, sure. Can I just click screen? Yes. Can you see, do you see on top that you can share your screen or should I... Oh, sorry, I need to make you present. Yes, yes. Now it should work. Yeah. Okay. Yes, there you go. So can you see my screen? Oh, it doesn't go to the slide. Okay, let's quickly go over it. So as Grantsets, the initial idea is that you plan for open access costs in your in your budget. So costs for open access publishing are eligible costs. Now, of course, if you make all your publication open access through an open access journal, especially in some areas, this can be expensive. So it's a good idea to combine strategies. The open access journals, often called the gold open access, if they ask for high fees, you can also opt for a subscription-based journal. So that's still okay. You do not need to publish in an open access journal. You can choose. But you always need to self-acquire. So if you do not have the funds, you can choose for a closed subscription-based journal. But then you need to self-acquire in a repository. Most universities have repositories. Most research institutions have repositories for publications. But if you do not have a repository or cannot find a repository, there are lists with repositories. Or you can use the nodo, for example, which is the European-funded repositories for publication and data. So it is possible to make your publication open access without any costs. You should check your publisher's policy if you do that. So most journals allow this. This is a common practice, good practice. But it's possible that you can deposit your publication in a repository, but that you can only make it open after six months. This is also not a problem. So there's an exception that allows an embargo of six months for open access. And for the social science and humanities, this embargo is longer. So that's 12 months. So if you do not have the budget, I would recommend to self-acquire in a repository and also look around for journals. Because there are several open access journals for article processing fee, or that have very low open access article fees, or that have waivers or things like that. So these are the options if you do not have the fees. Emily, maybe it is worth mentioning because I think people might wonder about the implication of something like initiatives like Plan S. I don't think we can discuss this right now, because on Thursday there is a webinar with Marina Angelaki, who is going to give an update on most recent policy developments and the likely influence of initiatives like Plan S. As you notice, this is an initiative by I think 11 funders in Europe who actually are more radical than what is European commissioners proposing right now. So there will be the embargo periods, for example, but this will be abandoned if this goes through. But as it is until now quite unclear what the consequences will be, and it is definitely not applicable for Horizon 2020 publications yet. I am not sure if anybody has any maybe Sarah, you have any updates of that, but I don't think there are at this point. We are all waiting a bit to see what the practical implications will be. Yeah, I don't have specific updates on that. There is one more question. I mean, Garrett has posed a question in the chat box, so maybe we can deal with that one first. And that is a question, because our next policy for literature has been around for quite a while. Building on this for Horizon 2020 can draw on that experience to implement whatever policy emerges. And there is a mature repository infrastructure to accommodate I'm not sure what that means, and the pre-print route and to an evolving business model from publishers. The concern that I've encountered is that a similar infrastructure for the long-term preservation of research data is not quite there yet, at institutional or national level. For an open science policy to be effective, this infrastructure gap will have to be bridged. It's likely to emerge to help us achieve this. So I think, in summary, will there be an equally robust data repository infrastructure? Will this emerge in order to support open science policies? Yeah, I think you hit on an important point, Garrett, because like you say, there are a lot of publication repositories and actually it's, I think, handling the types of publications because it's quite a lot of it's very similar what's being deposited. I think that's a simpler task than dealing with a wide variety of different types of research data. So having a data repository is a more challenging thing to provide as a service. There are definite gaps in the infrastructure and one of the recommendations we make in the Fair Day to Expert Group report is that there has to be investment to plug the gaps in the existing infrastructure and there also has to be support put in place so that those repositories can get certifications but also that they have kind of strategic and sustainable funding from funders or via other business models so that we know that they can preserve the data in the long term. Because a lot of repositories are actually on soft money and that isn't a way to ensure that the data is available long term. I think one of my concerns in this area is the growth of kind of publisher services to handle the data management for you or to handle the data deposit and I think we maybe need a little bit more reassurances about what will happen with the data in the long term to make sure that we're not just handing over the rights to the data and being charged for that in future the way we are charged for publication access. Yeah, thanks Sarah. Garrett is also on Wednesday we have Marian Grothfeldt from Downs talking about trusted data repository so that might also be of interest for the people who want to know more about data repository Another route another field of question is that do you expect that the EU intellectual property directors will support our follow open science goals? I think this is a very interesting question because with the recent commotion about the copyright directive and general being considered as not being very open science friendly. Emily or Sarah are you comfortable talking about copyright and legal stuff isn't my expert area so I'm not fully versed in all the changes there I mean we were involved in doing a response with spark Europe to kind of pick up on some of the aspects and I think it's important that we stress the importance of data sharing but you probably know more about the specific details than I do I think it's clear that legislation is not always suitable especially when it comes to things like text and data mining and the right to read and the right to mine things like that it's not self evident yet I'm thinking about what source I should refer you to if you want to know more about that hold on I'm going to look for it so maybe if there are any other questions in the meantime we can deal with those Yes it's clear here I do have a question a question, comment I'm a research scientist so I'm very familiar with publishing in the traditional peer reviewed journals Evolving Policy Government of Canada, Environment Canada we are freer now to publish in non-traditional venues it's emptied recently to put a pre-print in one of the pre-print servers so while we support this policy the devil is in the details and when you come along to actually try and do it it's not evident and there are a number of frustrations to overcome so for example, by the way I work in a policy shop now I work in the science and technology strategies directorate so trying to do this as a prototype or nail down a detailed workflow one of the issues that I came up against was since we work for a government and a crown then there's the added complication of crown copyright I imagine that national governments who are not under a crown have similar copyright policies also but the crown does not transfer copyright so for example, if I were wanted to something on archive or bio-archive there is a clear mechanism for selecting a Creative Commons license but not a crown license unlike some of the traditional journals when an author, university professor for example submits an article then you sign, you transfer your ownership of copyright with the journal copyright form and typically there is an option for authors who actually don't own the copyright because it's owned by the crown and the traditional journals accept that so there's a gap going the non-traditional route and I couldn't solve it at the time and so I ended up putting it on GitHub and linking it to Zenodo in order to get a DOI but still the details of the crown copyright are not clear and I expect that there would be similar instances in Europe so I was wondering if anyone has any experience with that and if they found any solutions thank you thank you very much for this this is a very interesting perspective does anybody else want to share their experience or their thoughts on this? I know again I'm plugging one of our other webinars but on Thursday we have a guest webinar by OpenMinted or OpenMinded and I know they've been working a lot around text and data mining and copyright so maybe that would be worth attending and maybe Sarah or Emily we do know some other projects or sources of information I think it creates from Glasgow University they're working a lot about copyright and intellectual property and OpenScience do they have a list of resources? yeah I think they do so I know they've developed some training materials and kind of like different kind of hards and things to try and demystify some of the issues I'm not sure if they've got something specifically on the type of work that you're looking for there but we can certainly add details to create oh you've put it already in the chat, well done Gwen thank you yeah so please if anybody in the audience has some more experience please share it because I think it's sometimes this whole copyright and the copyright intellectual property tends to be a bit of a black box and we always feel it's useful to hear different experiences and also to learn about like a relative importance that is that is being given to this topic in different fields and in different countries so if you have any more experiences please just drop it in the chat I don't see any updated questions on Mentimeter I'm not sure if anybody who's still in the audience does have any other questions or any new topics that they want to broach or just any specific questions for Emily about her tutorials I think it's okay to speak as well so maybe you can just unmute yourself and talk okay if this means that there are no more questions from you in the audience I'm not sure Emily do you or Sarah do you want to add something or do you want to give something to the audience of this of this session any final words of advice I think use the support that's there so use the opener network of national open access desks you've got contacts in every country so if you do have questions around Horizon 2020 on the open access or open data side you can get support there and also through the foster project you've got training materials so I'd just encourage you to make the best use of that support and kind of if you do get complicated questions pass them off and try and get advice elsewhere if you're not sure about how to respond I don't want to take up some more space but since there seems to be a lot of silence out there I do have another question yeah so the question is with respect to DOIs and I'm wondering when one publishes in one of these non traditional venues take for example Zenodo so Zenodo issues a DOI what happens if the article then goes on to be published in a traditional journal is there some kind of linkage or tracking when you have different DOIs pointing to the same resource the same article so for example a preprint or something on Zenodo and then the final peer reviewed article in a journal well I know that at least on Zenodo you can put related DOIs so actually I don't know if we can bring up the interface share screen and show the interface but you can flag a relation so you can say that this other object has been cited in the paper or is an earlier version of the paper or I don't know if this preprint is one of those relations but there's a whole category of relationships that you can flag I think if it was the exact same object if you were depositing in two repositories I'd try not to have two DOIs to the very same thing trying to actually provide the DOI as part of a deposit if your repository at your university forced you to have a copy there as well as you'll publish a copy try to make sure you just use the same DOI if it's exactly the same object so the publisher the traditional peer reviewed journal the publisher will issue a new DOI is that correct I think it depends actually because an example of this on Zenodo you do have publishers who actually just use Zenodo as archive as well so I'm not sure if we can say like if you can have like one blanket statement for all publishers right I'm sure not if it can be of help I just brought up an article on the Zenodo blog about DOI versioning by Zenodo himself so that might be an interesting read I cannot really give any more specific answers we can get you in touch with people behind Zenodo if that's something that you want to discuss more in depth sure I'd appreciate that thank you okay any other questions so I'll put up here a link to the preprint that I was talking about and the other question is about we support the concept of open science and moving toward fair data and so on but again the devil is in the details so both from the point of view of the data provider where data have not been collected and managed actually with the concept of open science as an end goal so then when you come to after the fact want to publish your data it's not evident that these data are going to be easily usable among non-user conversely when using other people's data that have not been prepared with the idea of fair data becomes sometimes problematic so I'll put the link oh no I can't put the link because my computer just died but they're sorry about that but it is in Zenodo it's if you search for a path to big data readiness as you'll find the preprint talking about so within quotes a path to big data readiness in Zenodo was put up last week sounds very relevant to the topics here thanks could you type it in chat because I didn't possible because your computer died sorry so did you catch the full title yeah there you go okay hold on so just while Gwen pulls that up does anyone else have any final questions I'll garrus put the link in the chat to the article thank you okay so thank you very much for this pair this is very interesting anybody else who wants to chip in no well in that case I think I will close this session and I would first of all like to thank you very much for for listening in and for asking your questions I hope it was despite this not being a traditional webinar format I hope this was interesting for you please feel free to share the tutorials and training materials with whomever you think this might be of interest in your institution or elsewhere we would really like to see these broadly distributed and if you have any additional questions about this webinar about any of the others you could either go to the overview page which you found because you're here or you can also get in touch directly at webinars at openair.eu so if you have any questions that now didn't come into spring into mind but that you think of later feel free to just contact us via this and we will connect you with the speakers here or with somebody else at openair or foster who can answer your question so thank you very much thank you very much Emily thank you for making the tutorials for taking the time for taking the time and thank you very much to Sarah from DCC for being present here as well and I'm looking forward to seeing you all and more of our other webinars that we'll be hosting later this week thank you very much thanks so bye bye thank you