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Downtown Brooklyn campus ​I'm really thrilled to talk about a really exciting endeavor we've been up to for the last year so as I mentioned I've had the pleasure of speaking here in my previous role in geospatial application building and got to show really cool things about climate change and I've spent more than 15 years of my life working in conservation biology and using maps and data to help people understand the environment and better know the world and and I've now taken on since last summer this new role of being executive director of this new data Science Institute here at Berkeley to even broaden that that further into almost any in every research field that's involved in scientific discovery so it's been a really wild ride I think it's pretty clear to all of us that nearly every field of discovery is transitioning from data port to data rich so from astronomy to oceanography biology etc there's just massive amounts of data out there and there's a lot of people doing a lot of things about it so on this campus alone there's been tons of efforts in previous years by really huge people and organizations around helping to build tools and engineer products and and builds communities around helping research science through what we are now calling data science by taking big amounts of data and services and and making great products and decision support tools around them so bids launched in december 2013 here in this room really didn't get started till i started in the summer after that in last july and we didn't even move into our new home in da library as I'll explain to last september's so it's been a really fast paced year where a lots been happening and this effort originally got started by a faculty group of superstars here on the campus that really represent a diverse background of departments and domains and we're led by saul perlmutter the Nobel Prize winner in physics who's our our director but we have 12 wonderful kopi eyes that represents in green a lot of the different domain sciences and in blue a lot of the different methodologies and so this all came about in a large part because of a large grant by the more in Sloan foundations for 12 and a half million dollars that really is interested in what they're calling the data science environment and how data science can help improve research and scientific discovery at an academic institution like Berkeley we also have industry support from Siemens and State Street and the university itself has really come through in a large way to help us for the more in sloan Foundation grants this award was also given to two other universities NYU and UW who also have similar institutes in their own institutions and we actually work very closely the three of us and look at how the problems that we're seeing here in Berkeley may or may not relate to the problems they're seeing in their respective institutions and we need up annually to to work together on all of these issues so some of the goals that we have at bids are to support meaningful and sustained interactions and collaborations between these science domains like life sciences Social Sciences physical sciences with the methodology fields like computer science statistics and applied math we're also very interested in establishing new data science career paths at the University that are long-term and sustainable so much like you're hearing about all of the hiring issues that they have in businesses there's actually a almost opposite problem at the University where we have a lot of very talented data science researchers who don't really have very good options if they want to grow in their field here at the University and I'll talk a little bit more about that later and lastly building an ecosystem of analytical tools and research practices so we have a lot of people who are building software tools and enabling scientists to spend more time focusing on their science but really when it comes down to is people and I think you've heard this a lot in the previous talks as well this is how we work we have a lot of great people doing a lot of great things and it's the community that really drives all this home so what you're seeing in the top row of pictures here are our data science fellows and most of these folks work with us at a 50-percent time model where we've taken folks from all over campus in different departments that were kind of the Rockstar data scientists of their departments and given them an opportunity to work with us at half of their time and these are postdocs and research scientists and they're able to now try to take that half of the time to do something bigger and better than they might have done should they have just been doing things business as usual in their own departments and the hope is also that they're learning from one another so we have an astrophysicist setting next to a biologist sitting next to environmental scientists and their learning from each other and realizing that some of the problems one had had already been solved in the other field and vice versa and taking that information back to their home departments we also have a number of senior faculty senior fellows on the bottom who also represent a large swath of the community here and everybody has been just really fantastic I took this list of people and created this this list of the different departments that people come from and it really drives home the fact that this is really diverse group and they're really working together here you know we have people from sociology nuclear science seismology computer science you name it and in addition to the

diversity of domains and methodologies the projects that they're working on and particularly in software development also have a large range of diversity so we have some of the folks building really amazing tools and are really amazing tools in Python Julia you name it and it's been really great to see the the influence that each other have on each other in building these tools well I'm gonna call out three really quick specific instances that I've just come up recently that I think exemplifies some of the great things happening here by our fellows katie hoff is a nuclear engineer who's been working with us this year as a postdoc she's actually just publishing this book called effective computation in physics I told her I'd plug it check it out it's really a fantastic resource that drives home a lot of the same hacking skills that actually transfer transfer beyond physics but in this case she's using all of these opportunities to bring home to her field in nuclear engineering the types of skills that are needed to do good work karthik graham has been working for a long time on this program called are open sigh in our which is a fantastic suite of tools that that does amazing things if you're involved in our I highly recommend you check it out as one brief example with a project I previously had an opportunity to be involved with in my previous life in geospatial world with with mapping and biological sampling the museum's here on campus have over a million specimens in their archives and many of them have been geo-located as to where they were collected and when and we've created an API around all of that data there's over five million records of specimen locations and that's one of many different kinds of data in this API called the Berkeley eco informatics engine if you're a programmer and want to spend a lot of time building something like an interactive web map you can do so but it's not the easiest thing in the world with our open side you can use this one line of code and generate a beautiful map in mep box that that's really simple lastly here project Jupiter you may be familiar with ipython and Fernando Perez is one of our co pies in this effort as well and he's a really big contributor to the bid's community and they him and his team of developers have been working really hard at converting ipython to this new Jupiter project and the idea there is that the interactive IP environment is being moved into a world where it's agnostic to language so Jupiter stands for Julia Python and are and and you can use the same interactive environments with all of these languages now and I like I said space is really important a lot of this wouldn't be able to happen without this really amazing space we have in right in the middle of campus into a library this map represents in yellow all of the different buildings in campus where data science is happening and of course it's just a campus map because pretty much data science is happening in every building on campus and so being right at the heart of that is really excellent and is it enables us to help build that community and bring people together so you walk in to do a library and turn left and we're right there and there's a lot of that it's a collaborative space and a lot of different great ways that we can use it what we share with the more Sloan effort between UW NYU and Berkeley are something we're calling the six working groups and the idea is these working groups are centered around identifying problem areas and challenges that with Intel connecting the scientific themed areas that I talked about and the data science methodologies and so their career paths and alternative metrics education and training software tools reproducibility working spaces and ethnography and evaluation and I'll just briefly tell you a little bit about each because I think they touch on a lot of the the problems and interests that a lot of these talks and a lot of both academics and and business folks have to think about so career paths this one is a little bit in specific to the University as I mentioned because we have some fantastic data scientists among our fellows for instance who want to remain in academia and have a tenure-track position as a professor but they're writing code and they're writing the software that everyone is using to produce great papers and publications but maybe they're not producing a lot of publications because they're writing the code and in traditional hiring committee that's kind of frowned upon so there's not a great path forward so we're looking at how do we build on alternative metrics to make these folks more attractive to traditional hiring committees here at the universities education and training is obviously important in all of these regards and there's many opportunities to do that on a campus like this and beyond with the web and we're hoping to build upon that you know most of the folks we work with our self taught and so how do we get beyond just offering opportunities to teach yourself but having activities like workshops and boot camps and different ways to to help people learn how to be a good data scientists software tools and environments this working group is led by Fernando Perez and they really are taking this open-source charge that's been fantastic to see and building this next generation of tools that researchers can use reproducibility and open Sciences led by Phillip stark chair of statistics here and it's really all about providing good opportunities and and stories and learning experiences to help people better understand how to do open science and open research and ethnography and evaluation that we actually have led by Catherine Carson small team of social science experts who are looking at data science from the inside and coming up with ethnic rocket ethnographic evaluations of of how things are working and what could be improved and how data scientists work together and work with others in the University and then lastly working spaces and culture is about the fact that we are all people and that we have a place that we need to work together and how do we come up with the spaces and the activities to to make that happen one

of the things that that has come to mind with this that's been interesting to hear especially from people who were coding back in the days where will you Ailee had to go visit your departments big computer and spend the night there to run your code and get your answers and we don't really have that anymore you can do it on your laptop but one of the things that was lost in that transformation from having go to the source to do your work to now being able to do it anywhere is just that one-on-one in the human into human interaction of hanging out with the fellow scientists and doing the work together and having those ideas those watercooler moments and so that's a little bit of what we're hoping we can reproduce here and then we just have a number of different more public facing activities that we hope can help engage both people on this campus enough we have a data science lecture series where we have speakers both come from industry and academia and share their experiences in this world there's a lot of great videos we've been recording here that you can check out on our website including mike mckowski from Code for America was here and gave a great talk last semester hacker within has been a fantastic group started again by kdf one of our fellows and Rachel slabaugh professor and nuclear engineering and they had a small peer-to-peer learning group that existed in meeting rooms for nuclear engineers to learn how to hack and we asked them to bring it to a bigger scale and include others outside of their field and they've done that and it's been a really neat way basically for people to get together and have a little bit of a lecture a little bit of a training and a little bit of a hacking session to learn all of these skills and i'm not actually started by a nice cool master student Anthony Swain is the data science collaborative where they have been taking undergraduate and graduate students and forming small teams to hack on a project for an entire semester that had been presented by different groups who have projects that they would like help with both all from government startups finance and scientific research and they just presented their findings this week and it was really fantastic to see these groups coming up with great data science outcomes but also learning in the process of doing it and that's all I have today but check out our website and I'd be happy to chat with any of you I know y'all have been had a long day so I'm happy to chat offline too kind of a separate entity from here so how much of your training boot camps those accessible to the mates program and how much can you take advantage of our also contribute they're open to anyone here at the University and I would love to see more mid spokes get involved I will be working with software carpentry in particular on the boot camps and so you know they do workshops all over the country and so some of our fellows are very involved with software carpentry and we'll be doing at least two or three boot camps a year that are coming right out of their curriculum New York University of Architecture.

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