 Thank you, Erin. Okay, I have to be the first glitch of the day. Hopefully that's it. So I'm delighted to be here. I have the very simple task of describing the large and growing body of research regarding participants perspectives on data sharing, and also touching on the evolution of genomic data sharing policies in 10 minutes or less. And so I might a bit anxious about this. Yes. But I think it is important to set the stage. So I'll take a few minutes for that. And then I think I found a shortcut that will help me in terms of capturing findings from research. And if all goes well at the end, I'll have a minute or so to touch on some possible implications. So I think in setting the stage, it's very important to consider whose perspectives were capturing. If we want to go very broad, we can talk about the public as the population of potential participants in research. Within that group, we also have patients, people who are currently receiving care and healthcare systems and so have electronic health records, for example. A very important group is individuals who are affected by serious health conditions and have limited or no good treatment options in the case of children, their parents. And then finally, we have people who are currently enrolled in research. And while there may be commonalities across these groups, we should also anticipate that there may be some distinctive perspectives. Now this is a very selective timeline showing NIH policies along with some events out in the world. And I think one thing that becomes clear is that the field of genetics and genomics has been in the forefront, has been leading in terms of promoting researcher sharing of data through policy. But there have been some swings of the pendulum. For example, in 2008, we have the first of a series of papers showing that it is possible to identify or re-identify at least some individuals from pooled data. And in response, the vast majority of genomic data, including genomic summary results, are moved into controlled access. But we still have policy moving forward in favor of open and responsible data sharing. And in 2018, most genomic summary results are actually moved back into unrestricted access. So I just want to spend a few seconds at each end of this timeline. I began in the 1990s because I wanted to remind myself to convey to you that some of the most passionate promoters of data sharing were patients and patients advocates. And in particular, parents of children with serious health conditions and limited treatment options. And they became frustrated with the data silos and the outright data hoarding on the part of some researchers and were moved to create their own data resources. So here I give two examples, the Autism Genetic Resource Exchange and the PXC International Blood and Tissue Bank. And then if we move to the present, the NIH just released the policy for data management and sharing. And in that document, there's an acknowledgement, a recognition of tribal sovereignty and the need for tribes to control their data. And you might characterize that as a swing of the pendulum back, but I actually think it's an absolutely critical step forward if data sharing is to move into the future on just and equitable terms. So this is my shortcut. I'm very fortunate that in 2016, Nanaba Garrison and colleagues published a systematic literature review of perspectives on data sharing in the United States. And this captures a somewhat eclectic mix of studies, but there are some common threats and I think they're nicely captured in this paragraph. So first, the majority often expressed support for broad consent or consent to broad data sharing when that was the only choice offered. But if you didn't give people more options, including options that would give them more granular control over the data, they tend to prefer that. Also, willingness to give broad consent increased if data were described as de-identified. And secondly, in terms of data recipients, individuals were most willing to share with academic researchers. They had concerns about sharing with federal databases or the government or with commercial enterprises. And in some work we did subsequently we also found heightened concern about sharing with researchers in other countries. This is a study that came out in 2018 focusing specifically on the perspectives of clinical trial participants. And they tended to be very supportive of data sharing. And the rationale that resonated most with them was maximizing the scientific benefit from their participation in clinical trials. On the concern side, it's very interesting, the top concern was that knowledge that data would be shared would decrease the willingness of other people to participate in clinical trials. And then to get the global perspective, this is a publication that came out just last month from the your DNA, your say survey, across a number of countries, looking at public perspectives. And as you can see, this is a graph of percentage willing to donate depending on recipient. The ochre bar is doctors, the blue bar is nonprofit researchers, and the green bar is for profit researchers. And across most countries, doctors and nonprofit researchers are roughly on par between 50 and 70% willing to donate in no country above 75%. Interestingly, but there's a substantial drop off when you specify the recipient as for profit researchers says consistent with some of the work that garrison and colleagues captured. Now another way to look at this beyond data de identification is in terms of the features or contextual factors that might influence people's willingness to participate. So we're looking at the influence of compensation and especially return of research results. And so even people who reported that they were very concerned about privacy could be moved to participate by being offered greater compensation, or by receiving results. So in the initial condition, where they only get $50 well $50 to my kids is a lot but they get $50 no research results. It's 47% but that jumps to 58% when research results are added into the picture. This is the last piece of empirical research that I want to put out there and it's a bit different. It's research I was involved in a series of three deliberative engagement sessions in cities across the US. And we were looking at large scale data resources and we wanted to know what would make these efforts trustworthy in the public view, and especially where their features that we could recommend toward that end. And we sure it out by just asking about their hopes and concerns and they they did have great hopes, as well as significant concerns about these efforts. But when we turn to something like governance I think it's really interesting to get a bit more concrete. So we presented our deliverance with deliverance with a spectrum from no involvement of participants in governance to participants really running the show. And most of our deliverance settled on number four. They wanted participants to have seats at the table when decisions were being made. At the same time they recognize that just a few people couldn't really represent the perspectives of thousands or millions of participants. And so they also advocated for doing things like number to having feedback through surveys informed decisions. So finally as to implications at just underlining that data hoarding violates the expectations and wishes of many participants. Most do prefer to be given some choices and have reservations about sharing with for profits in the government. But in practice, most people seem to be willing to consent to broad data sharing. And I would suggest and if this is controversial we can discuss it in the Q&A and the nuances but not accommodating all of people's consent preferences is not equivalent to violating their rights. At the same time, if we do move forward with a broad consent paradigm, I think it's really important to consider those contextual features additional steps that can be taken to increase comfort and trust, demonstrate respect and establish trust worthiness. For example, returning results or other ways of returning value to participants, taking care around access rules and other aspects of governance and being vigilant about privacy and security. And these measures are especially important if we are aiming for more representative data resources than we have currently, which I think should be a high priority. And finally, even these measures won't be sufficient if we're talking about groups with special reasons for concern, say for example due to a history of exploitation or mistreatment by researchers, or if we're talking about especially sensitive research, for example, research that could lead to stigmatizing certain groups. So in those cases I think we need additional measures both to involve participants and to protect them. And that concludes my presentation.