 What we are trying to do is trying to make a better use of data. There are plenty of data which exist in the world, but for a certain proportion of diseases, they are not probably optimally used. And we are in this business of evidence ecosystem where we are trying to make more sense of all of that. So coming back often to this slide, but I think it's illustrating very clearly the problem that we are facing. If we compare what we call neglected infectious disease, and I'm not talking about neglected tropical disease, which is a group of disease which is probably limiting by WHO. But if we try to compare the neglected infectious disease versus the disease which affect the wealthy world, there is one thing which is striking is that the volume of data and the volume of clinical trial is massively different. And this is the area where we are focusing on. And it goes down to a ridiculous number in terms of this research, which we can probably make a parallel ridiculous number of funding associated to that. And so we know this inequity, but what can we do with that? Well, we can collate standardized clinical pharmacology and molecular data. And that's what we have been doing for about 15 years. And to try to combine that in a single data set which increased statistical power. So the model is fairly simple, or the idea is fairly simple, but the question is how you do that. So one thing, this is a selection of a systematic review that we have done in the context of a drug efficacy. And when we are trying for all these diseases to measure how many trials exist in the world, which are actually the support for evidence, well, for guideline treatment and evidence gathering. And it's shocking. This is something around, so it's not all 30 years, but close to 30 years. This is the number of efficacy trial. And that could be randomized clinical trials, minority, but more single arm trials, which are the support of the current therapeutic guidelines. And that's really, really bad because that's translating to evidence which are not very strong. The data are scattered around the world, sample size are small. And I would say this is true for these diseases, but also it's true, for instance, for COVID in the context of low and middle income countries. So that's what we call, sorry, our roadmap. And what we are doing when we start working on a disease, we don't claim that we are the expert of the disease. We are rather claiming that we have a know-how into data gathering and data platform. So we scope how much data exists or studies exist. We try to develop with the community a clear research agenda of what we could do if we were to gather this data together. We do the curation of the data, so the standardization. And we do the IPD meta-analyses, or we do it in collaboration with that research community with the idea that eventually the work will translate into a guideline and something that policymakers will take on board. One thing that we are putting quite a lot of effort is the equity in the business. Because essentially doing that from Oxford and expecting that this will be picked up by the communities in the various countries where the disease is endemic doesn't work like that. And there is a stronger effort and a stronger effort that we are making over the years in engaging the community and the people who are in the first place generating the data. So there is a lot of engagement, which is something a bit tricky because on the one hand, founders are happy to fund the platform and the IPD meta-analyses, so the science bit. But they don't want to fund this collaboration and this engagement with the community. But I'm glad to see that there are programs like AFOX and Overs that we probably should be working more closely to engage the community and making sure that actually the people who are generating primary data are also part of that effort. But right now they are, but it takes a bit of effort. Briefly, just to illustrate what we are doing, so we gather data from all around the world in shape and format that they have been collecting. We standardize that into a CDISC standard, which is a recognized standard for gathering data. The data are then in the safe repository and then we do, we do or Overs will do IPD meta-analyses on the basis of that. But let me give you an example of how that has been picked up in particular by WHO. So very briefly, you know that the treatment of malaria is Artemisidinine combination therapy. The first drug which has been of the ACT, which has been registered was in 99. The first dispersal formulation of the drug was done 10 years ago. And in 2021, but one billion Artemis terliumphantrin, which is the main ACT, which is currently on the market to represent probably 70% of what is on the market has been sold. That's a lot of treatment in the space of about 20 years. Now, if we take in particular the example of a pregnant woman, clearly WHO highlight that this is a group with particularly at risk of a feeling treatment or having severe form of treatment. Nonetheless, it took quite a long time before WHO would recommend a treatment for pregnant woman, which was based on Artemisidinine combination therapy, not because they were particular fear of that, but because they were very little data. As opposed to the recommendation was quinine. And quinine was not because it was based on very solid data, but because quinine, which was registered more than 70 years ago, saying that it was fine and safe to use in pregnant woman, but certainly not based on very solid data. So in 2015, WHO recommended the use of ACT or in particular Artemis terliumphantrin for the second and third trimester of pregnancy, but this evidence was named strong recommendation, low evidence. And so we did an IPD meta-analyses which was published in 2020, which is showing a better strength of evidence in that. Now, we are just currently starting a discussion with WHO to convince them that actually this is creating a stronger evidence and this should be highlighting in their guidelines, but this is a strong recommendation with strong evidence. In 2022, we did a second IPD meta-analyses which was in the first trimester and this has been picked up more rapidly by WHO and it's now in their guidelines as a strong recommendation, but still with low certainty of evidence because the volume of data that we are dealing with is limited. So this is just to illustrate that, yes, we can do this kind of job, but it takes a lot of data, it takes a lot of engagement. Clearly, there is this particular IPD meta-analyses was looking at a thousand women who have been exposed with an ACT in the first trimester. If you recall, there's one billion treatment which has been sold, there is probably a bit more than a thousand women who have been exposed, but that disconnect in between the reality and what we can do and how we can accelerate that because on the other hand, it's not very satisfying that it's taking 23 years to get to that recommendation. The point on impact, what we are also trying to do is to engage with the community and making sure that this effort and the use of this data or the reuse of the data is not only done by us, but is done in collaboration and led by colleagues who are sitting in LMIC. So this is happening as we speak and we are now reaching a point where this is split in between two. So there is half of the investigator who are leading this work who are coming from LMIC. We hope that this will increase. I think there is a lot of capacity building that has to be done. IPD meta-analyses are not necessarily very complicated, but there's still something to be done in terms of training and support for doing that appropriately. In terms of capacity strengthening, we are working in different areas on that. So we are working with TGHN and Moru, in particular with EDCTP and Network of Excellence in training and providing a data sharing curriculum for EDCTP African networks. And that's really important is to try to give a guidance on how you do that. With TDR, we have had for many years, TDR fellows who have been joining us and then have gone back to their home institution with whom we have collaboration. We have been working on a project called SORT IT, which is supported by TDR and which is training people on the ground. We have a collaboration with the Indian Consider Medical Research where we are working with three institutions of ICMR, also supporting young researchers to do the kind of work that I described. And I'm also very glad and pleased that AFOX have been supporting fellows, senior fellow from Sudan, who considering the current situation in Sudan is not able to do his work while actually this particular area of work is very neglected and his expertise is badly needed to continue working for this particular disease, which is visual leshmania disease. And that's my last slide, just to highlight the three areas that we are putting a lot of effort that I was trying to illustrate. So equity, we think that this is extremely important because if we want to become a data, a global data platform, it comes only if we are putting together an equity mechanism, the science. It's about doing this relatively sophisticated analysis, but we can do more. We don't have the capacity of doing everything. So bringing more people on board is critically important. And we've got a repository which is now functioning on a relatively large set of data. And this is just to give you a flavor of the different diseases that we are working on. But very happy to discuss further with you. Thank you.