 Okay, so this is obviously about preparing students and a workforce for the oncoming data tsunami, which is already upon us. Let me give you the backstory to this whole iNURL project. A few years ago I had the pleasure and privilege to go and serve at the United States National Science Foundation and there I heard a talk by Sean Hill who talked about neuroinformatics and this really piqued my interest. So let's take for example the studies we heard about this morning on macaques and marmosets. Those are very expensive studies. They're very time-intensive and as somebody who has spent a lot of time peering through microscopes I bet they're a little bit tedious for the people actually doing the work. So I'm a neuroscience educator and there's no way in my 10-week course we're gonna do anything like that. But if I can grab that data and have my students analyze it in a new and interesting way that's a very valuable resource. So that's how I got into this world and so right after I left the National Science Foundation I had coffee with a couple colleagues, former program officers there at the National Science Foundation and they said you know we're gonna have a data tsunami. I'm like yeah it's probably already here. You know we are not training students to deal with this data tsunami. I was like yeah you're right but we're not. And they said you know if someone were to write a grant to hold a conference about what that curriculum of training would be we'd fund that grant and then they paused for a real uncomfortable long time and stared at me and I got the hint. So I wrote the grant and put together the conference and so what I'm going to do in this presentation is talk about the recommendations that came out of that conference and where we are now and at least in my opinion where we should go. So I didn't have the foresight to actually take a picture of the participants of the conference but it looked a whole lot like this and Marianne was there I think you can find the one that's representing her in there too. So I got together all these smart informed people to think about this curriculum and how we should train students. So the cast I had managers and purveyors of data resources for example I had people from the Allen Brain Institute. I had people involved in bioinformatics training thinking perhaps neuroinformatics training could use them as a model. We had library and information scientists, computer scientists and of course people like me who are neuroscience educators. So we started by talking about the skills that they would want this workforce to have and these slides are actually color coded so anything in yellow was something that came up repeatedly. So what they said first off is it shouldn't just be some computer scientist who has no idea about neuroscience they need to have a neuroscience background they need to understand methods they need to understand experimental designs and a lot of sometimes people said they even should have some wet lab skills. They decided that they needed technical computing and analytic skills and particularly coding ideas that came up were things like translating data revamping stewarding curating and hacking which I'm not sure has a very firm definition but perhaps you can talk about that later. They also decided library and information science was skills that these kinds of people would need. So if we're to have fair data we need to have people who can make the data so it's findable and accessible and these are the kind of skills that would come in from library and information science and of course they would need to have quantitative skills for data analysis, building machine learning, my favorite probability and statistics, signal processing and modeling. And one thing that kind of surprised me and I wasn't very interested in it initially but now I think we should be more interested is what we would call soft skills so management skills and team building and teamwork and also ethics came up and I'll come back to this topic when I talk a little bit about industry. Regardless they said that the instruction should have hands-on practices and students should be using real data and try to be answering real questions in their instruction. Okay so the curriculum the participants decided on two levels and I know this doesn't always match and map real well on European kinds of educational structures but they had a bachelor so four year and master's two year graduate school or two year master's and then a PhD. So I also thought that they would say that this was going to be one kind of individual that would come out of these programs and instead they decided they would have three kinds of individuals that would come out of these programs and I had an artist draw these fantastic beasts because they are to represent that the kinds of skill sets and combinations of skill sets that will be needed are nothing like the stuff we have right now and we're going to have to think about jobs in the future and positions in the future that maybe don't even exist yet. As an example I do things in use analysis now that I never studied so something came up like PCA analysis earlier today when I was in grad school they said well there's this thing called PCA analysis but don't worry you'll never have enough data to use it so that's not true. So as I said they came up with three kinds of positions that they envisioned and the first is what they called a wrangler or a plumber. This would be somebody who got the data captured the data and got it into the system and the participants thought that someone who had at least two years of grad school ought to be able to do this. This one they called a curation professional practitioner or a data steward. Now you probably would be surprised to hear that scientists could not agree on a terminology for something but they could. So we ended up with three terms and so this they thought would need a master's degree possibly a PhD and then something that would sound more familiar to us a computational neuroscientist that would of course have a PhD but also as this winged and horned to be shows maybe need some more skills that aren't currently in our usual toolboxes. So those are the recommendations at least in broad sweeping terms of the workshop so let's talk about where it went and where we are now. We did manage to get a white paper out if you work for the National Science Foundation or do work for them of this kind they love to get a white paper out. If you want to see it in all detail particularly all the details a curricula that have been laid out by this workshop if you just Google this part right here M-D-C-U-N-E and look for the iNurl logo on that website you can get everything you need. And along with Linda Lanyon of the INCF we got a paper out that we published in Frontiers in Neuroinformatics. In this paper we took on the question of and at least as I'll say later there are very there are basically no programs in the United States that are doing neuroinformatics training. So there are however splinters of programs that could be knit together and create one. So there are schools that have database administration, a lot of programs in bioinformatics and computational biology. Neurosciences become almost ubiquitous and a lot of schools have information study. It's rare that a university has all four but a lot of universities have at least some of these. And I should point out that this paper came out about three years ago very similar in time to when the Brexit vote occurred and I am proud to say we are made a lot more progress with this paper than the Brexit. So I'm actually very proud that the Frontiers keeps some metrics and this paper was read more than 63% of the other Frontiers articles and we've had almost 3500 views of it. So the word is getting out. Again it's only three years old so we haven't seen vast sweeping changes yet especially I don't know what it's like in Europe but in the United States deans and programs work at kind of a glacial pace and you don't just pop in with a new program. Okay so in getting this talk ready I was looking for extent programs on the web. There's one at Edinburgh, there's one at Erasmus Mundus, Newcastle, University of Zurich and no surprise the University of Warsaw. By the way if your program isn't up here and it exists I do apologize but you need a better web presence because I couldn't find you. This one here at the University of Warsaw has both graduate and undergraduate training and so the speaker after me will talk about it and I am anxious to hear how they do it. My point however is right now neuroinformatics training is very Eurocentric so we're looking to you for models of how to do this. Another thing another place where we are now is the INCF resource expansion the training space it's really a lot better I want to thank Matthew because he has done a great job with that he put in a lot of work it's well organized the production quality on these videos is really slick and nice. We also have INCF supports a Google summer of code we have software carpentry Arial I think we'll be talking about and in person courses they sounded great for this workshop I was really sad I couldn't get here in time to do them so the INCF I think is filling in a big gap that is left by traditional sorts of instruction. Where I think we are though is what I call the 20,000 foot that's 6,096 meters to two-foot gap so we have a lot of stuff that talks about how great neuroinformatics is and what all the things can do but for the people who are actually still on the ground and trying to get off the ground with this we people not so different from me need some instructions like okay at this point you need this line of code and that's the kind of stuff we don't yet have. We are getting close though to getting that two-foot that's point six one meters we have a lot of nice things that are out on the INCF where website in training space introductory kinds of things statistics things like that it's just some background stuff that virtual brain stuff looked great I I hope to get further into that I hope I can adopt it so we do have some things that are getting to that two-foot level where people are trying to get off the ground on this stuff okay so that's where we are now where should we go I think we still need to fill that gap between the 20,000 foot level and the two-foot level and that's I think gonna be I hope one of the focuses of the training space and the training education committee. One of the problems that I think happens in the United States is at least our undergraduates in neuroscience and biopsychology hate math and so I think we need to push them and push programs to embrace computational neuroscience they're gonna be in a quantitative world like it or not and I think that that's something that we need to do along those lines the Society for Neuroscience in this upcoming meeting on October 21st is going to have a workshop on teaching computational neuroscience I put it together and we're gonna have Rob Cass, Adrianne Fairhall, Pascal Wallach who I think is going for that matrix kind of look here, Walt Babyak who is a colleague of mine and the privilege of teaching with him a favorite of many Matt Abrams is going to speak my co-organizer Richard Olivo and me so this is up and coming I think it's something that we need to get into our undergraduate curriculum. The third thing I think we should do is look to partner with industry and business and I know we'll have some speakers later. Business is really getting attracted to neuroscience this is just something I found on the web they at least believe that knowing something about neuroscience is gonna make them a whole lot of money. This is a course that's offered by the Wharton School at the University of Pennsylvania and to register for it if memory serves it was 4,000 US dollars to be a participant in this workshop so somebody thinks that we have some very valuable knowledge and we ought to be talking to them because we do have valuable knowledge and we got to figure out how to market it. I was lucky enough to go to a meeting in Toronto that the INCF put on and this was integrating neuroinformatics research with industry and so the industry spokesman talked about the kinds of skills that they thought they would want to see in a worker and I thought oh they're gonna say they want this kind of computational skill or that kind none of that came up not a bit of it they said what we really need are soft skills we need to find people who can manage they can manage people or projects or especially products and they weren't finding that in their workforce so soft skills turned out at least at that meeting to be very important I'll be interested to see what the people here say and I think that will do it I told you the recommendations of the I neuro workshop told you where to go for more information you can either go and download it or you can get that article from frontiers and neuroinformatics where we are now and at least in my opinion where we should go