 Okay, thank you very much. The second presentation will also be given by Luis. It's entitled research experiences and genomic research for data scientists. Go ahead, Luis. So the second concept, as I told you before, we'll mirror the first one on next slide. And this concept, we seek to increase the involvement of individuals who have completed a master's degree in an area related to data science to provide them with exposure to genomic research projects and the research education needed to fully integrate them into the research teams. As with the previous concept, this concept will provide funds for domestic institutions to develop research education programs that will support participants with master's degrees who are not currently supported by research grants. The research education programs will integrate participants into genomic research projects and provide them the necessary professional development to make them into genomic researchers. Next slide. As before, we propose to use our 25 mechanism, which will allow us once again to target those individuals that have concluded their master's degree. The concept will pay for the same costs as the previous one, so faculty salaries, participants' salaries, travel, so placing it for research and will provide the institutions with an 8% indirect cost. And we expect data scientists to find phenotypically-relevant signals and help communicate the findings of their research to their professionals through data visualization. Next slide. Again, the budget for this concept is identical to the previous concept. We propose a 225,000 total budget per year to support at least two participants per program. The programs can support participants for up to two years at a minimum of six person months, and the grants will have a maximum duration of five years. Next slide. I want to thank the members of the training team once again for developing this concept, and I will welcome your questions. They are discussion leaders with these concepts are Dr. Eideker, Kirisati, and Craven. Dr. Eideker, will you start the discussion? Yes, absolutely, thanks a lot, Lewis. So first of all, I'm very supportive of this general idea. I think it does fill a niche that is not filled by STEM master's programs. For instance, in computer science or math, they graduate a lot of people now, especially with the uptake of these STEM master's programs in those disciplines, but one suspects that most of them are not trained in genomics, including a few of them, and maybe many of them who want to be. So I very much appreciate the niche. If I have any comments, I think you know what it's going to be from our previous discussions. I would have a concern that the overall budget may not be competitive with what these people can fetch in the market, at least the very good ones who of course, you want to recruit, what these people can fetch on the market at the Facebooks and Amazons and like institution or like companies of the world. So that would be my only comment for further discussion and response. So the ballpark figure that we are using for post master's data scientists salaries that are working in academic labs is 60 K to 90 K. In the budget, you can support somebody for around 50 to 60 K and they don't have to dedicate 100% of that time to the project. So we should be able to at least in the academic salary world be competitive. So just to follow up on that, so I can imagine a case where as the PI, you're going to fund this person partially off of this award, this R award and then partially off of other awards in an effort to boost your salary up to something that's competitive. But the concern is if you have a STEM master's, especially in a discipline like computer science from a premier U.S. university at the top of your class, you are going to get much higher salaries than that. So you're going to have to really, really be dedicated to this idea of getting exposure to genomics, but without an actual degree being granted by this program, it's unclear what the value of that extra education is going to be versus just getting a much higher paying job. But like I said, please don't let these concerns temper the overall enthusiasm for the concept. Dr. Vizari. Yeah, so I'm also very supportive of a general idea. I think we need to increase the data science workforce and SPRUM is a good step forward. There's more nuances in genomic research that can only be learned with hands-on experience. So you can't really learn it in class. So I think this is a very, very good idea in general. Now developing and running a program will be quite a bit of work and the current funds. I share Trey's concern regarding the budget, not just for recruiting paypersons, but because it will be work and cost to develop a good program. And apart from the size of the budget, my other concern is that we're limiting it to those who already have masters and I think we could have a bigger impact if we use programs like this to subsidize people that are into completing a master's. They can learn, they can gain the experience while doing that. But with that said, I think this is a great idea in general and I hope we do more of things like this. So thank you for that, Vizari. Dr. Eideker. I mean, Dr. Praven. Yes, I'm also very supportive of this program. One comment I would make is that if the R25 mechanism will permit it, I suggest considering opening these to students who are currently pursuing a master's. And there's a couple of reasons for that. I think one is that if you think about trying to attract students who have finished a master's in data science, as pointed out by Trey and others, they're already facing really outstanding career options and great salaries. And I think if you think instead about trying to get students who are currently pursuing a master's in some data science field, that you can hook them in genomics before they've gone out on some other career paths, some other lucrative career path. But also I think you can probably stretch the funds further if you think about trying to support some, but a stipend for a master's student as opposed to a salary that's going to be competitive for someone who already has their master's. Thank you, Mark. I see Howard Chang and Trey again. Howard. I also want to agree that this is, I think, a very important and perk I'm going to support. I think these are the data scientists are a group of talent pool that we really want to tap for genomics research. And they're highly sought after, highly competitive. So I think for the pool, that would be individuals with a master's degree already. One idea along the line of Trey's concern about the competitive salary is that we may lower the requirement, the time commitment to be in this program for a master's level scientist. I think that would cast a wider net and may get possible for very talented individuals to actually also participate in genomics research. And secondly, I also want to agree that I think extending this to master's students, I think this is actually a pool. We have the highest chance of recruiting people at the beginning of their careers to enter into genomics research. That's a good opportunity, possibly at a lower cost funding level. So these are some ideas for consideration for the staff. Yeah, so just to follow up on some of the comments, I think what's exciting about this concept or one of the things anyway, is it immediately gets you thinking how you as a PI in data science with your genomics would use this concept. So one thing we would do, for instance, is walk over to the computer science building where they have recently launched brand new master's programs and greatly expanded their number of students per year. And you would poster that first floor of the computer science building with exciting opportunities in genomics for graduating master's students. And so all of the sort of comments that I made and any concerns come with just kind of strategically thinking that through how do you then really use that job advertisement essentially as a tool that's gonna be successful. Sharon, did you have a comment? I did, and a point of clarification. Again, as a T32 PI, if a student's on a T32, we can use no other federal source of funds to support them. So we can't use our research grants. Does that apply to this R mechanism as well? Because that would really limit saying that the other half of their time is being supported by your research grant because if that's an NIH research grant then you can't actually support them. We will need to check the small print in the I-25 to see what we can allow or not allow. Okay, I think on the T32, it's because it's considered a full-time position. So it may be because you give partial support here. Are you designated as 50%? But I think it's an important thing to check. Okay, thank you, Cheryl. Are there any other comments about this second concept? Okay, can I get a motion to approve the concept? Move it. And a second. Second. Second. All in favor. Aye. Anyone opposed? Anyone abstaining? Thank you very much.