 So, in the spirit of our day today, I have only three slides, but I have ten conflict and biases slides as a preface, and part of this is because of my interest in the history of science and medicine, and I wanted to set a little bit of context and go over a few things that haven't been mentioned today. And these are some of the projects and organizations which I work for or founded and continue to do work on. And so, yeah, so a few salient biases and background. I am in the camp of being more excited than terrified of DNA sequencing technologies. So you can count me in that camp, and I think that it really, that this is a technology that is the modern day X-ray vision glasses. And just to review a little bit of history, there's sort of one model of DNA sequencing adoption out there by citizen scientists which is trickled down, which, you know, it's driven by this chart which you've seen all over the place of the rapid declining cost of DNA sequencing, but DNA sequencing started in elite labs only, you know, only this is the Whitehead Institute, MIT, and, you know, not too long ago, ten years ago, if this was about the only way you could get access to DNA sequencing technologies, but boy is that changing. DNA sequencing devices have iPod docs or iPad docs, iPhone docs, they're getting small and portable and usable and disposable, low cost, and there's this whole other thread that's happening out there that's been mentioned just a little bit with do-it-yourself biology and the biohacking community, and we might call that trickle-up innovation. So this is a data point, 2013 UK Young Engineer of the Year Award has a PCR device in his basement. So this is the actual sort of adoption of these technologies in the wild, and the rate at which this is happening has been really dramatic with not just PCR devices but the whole sort of biotechnology tool chain and low cost laboratory devices, and so here's one just from a few weeks ago that raised over $200,000 on Kickstarter for quantified QPCR, and there also are communities associated with these technologies. There are communities of practice out there, and one of the things that I do is to build communities of practice around new technologies, and to start on the data side, this is one in Boston called HackReduce, and that for those who don't have the institutional resources of Google and the ability to actually get, you know, crunch and process big data, you can now go like a gym membership and pay a fee and get access to big data computing power at places like HackReduce, or on the biotechnology side, you can go get access to advanced biotechnology classes, hands-on training, run your own experiments, at community labs like GenSpace or Biocurious in the Bay Area, GenSpace is in Brooklyn, New York, and so I've been part of this community for a long time, and I'm a founder of DOI Bio, and then this sort of, one of the things that I work on is this Ask a Biosearch expert, which touches on our conversations about IRBs in some sense and equitable access to oversight, and so here's a model maybe you could apply to human subjects research or some version of this, where there are actually professional biosafety experts who typically are attached to an institution and they only care about sort of the perimeter of the institution and biosafety happening there, but they are the experts in the world, and they are providing free biosafety services to the biohacking community through more or less a biohacker hotline, and so this is very much still in the prototyping stage, and we're going V2 on it this year, but what worth what didn't. Anyways, I spent most of my time, this is my biases, since 2007 working on the Personal Genome Project, and I set these up now in four countries and a bunch more underway, and it's sort of a unique set of features, not any one feature is necessarily unique, but taken together are a global network of research studies and participants and scientists that I think create something very special. So now for my actual three slides, I really, you know, recognizing that it's the end of the day I felt like it would be worthwhile just to sort of pick one thing and focus on it, and the one thing that I would like to focus on is this concept of equal access, and I don't think it's ever been defined, but it should be, so here's an attempt at it, and this is I think a style of governance, which is out there, which is where the research study sort of, there was an amazing quote, I can't believe I'd never heard this before, which was, I'm trying to remember how it goes, nothing about me without me, I'd never heard that before, this is basic. Really, it is incredible. Okay, got it. So equal access is essentially that, in short, it's sharing by default, and this is something which I think holds tremendous promise and is absolutely a necessary prerequisite for doing actual civilian or citizen or science or being honestly engaged in a research enterprise is that any data that's generated about you is also available to you, and so a new project that I'm working on with co-founder Madeline Ball, a research scientist from trained at Harvard, and then a software engineer, Bo Gunnerson, is really is going to explore this and help to assist researchers with implementing this governance in their research study, and then also rewarding them for doing so, and so sort of one customer of this new program, which launches in one to two months, we're in a private alpha right now, is on the other side of the sort of the customer of the service is the actual individual who wants to participate in research, and helping to give them access to their data and do things with it, even if it's just simply to archive and store it privately. So now, this is the real slide that I wanted to get to, and as the end of the day as just sort of to be provocative and as a citizen science of research governments in many ways to sort of propose six hypotheses about equal access and things to think about, and one thing that many of these things to me seem really intuitive, and that's why I've chosen them as my hypotheses, and I'm not sure that they've ever been sort of empirically studied or proven, and maybe some of you have done this work and can come talk to me, have towards and point me to references, but if not, it should be done. One question about equal access is, you know, will we actually have more reproducible science when the individual who is engaged in research study is able to do error correcting or fact checking about what a researcher claims to know about a person? And then two, there's this whole concept that we have about research literacy and whether or not citizen scientists are really qualified to act in many different ways, and how are we going to improve research literacy unless we, you know, open the kimono? And I think equal access is an essential part of that. And also, how can an individual make really good informed sharing decisions without having access to the actual data? Number three, I think reciprocity is powerful, and I would hypothesize that in these models that there would be much higher recruitment, participation and retention. For overall, more data will be shared, although it would be more varied in these models. Number five, that we can demonstrate over and over again that this concept of cognitive surplus is real and that people who are not credentialed actually have meaningful contributions that they can make. And number six, and this is one I really should have started with, but my hypothesis is that the sort of research enterprise will go the way of medical care and that access to individual level raw research data will be a federally protected right as it is for medical records. And we should all be asking ourselves, why do we not demand more from the research enterprise when it comes to the ability to request access and audit the records that are held about us? We do so in medical care, and there's a very long history, decades long history, of advocacy around patient-centered healthcare and the important role of having access to medical records about that. And I think the same can be said and will be shown to be said or will be shown in the research enterprise. And those are my three slides. Thank you.