 Welcome everybody to this last session of the second day of the CNI 2024 virtual meeting. I'm Cliff Lynch the director of CNI and I just have a few quick introductory things to cover before turning it over to our speakers. We are recording this session and this will be available after after the session is complete through our usual channels. There is a chat box and you are welcome to use that feel free to introduce yourself if you like or to comment as the, as the presentations proceed. I have a question and answer tool on the bottom of your screen, and please use that at any point during the presentation to queue up questions. Diane Goldenberg Hart from CNI will be coming on after all the presentations and will moderate a question and answer session based on the questions that come in there. The next thing I will mention is that normally these sessions do have closed captioning available but there seems to be a glitch with this one. We should have closed captioning on the recording and we may get it working here during the session if we don't my apologies. That's everything I need to say in terms of mechanics. At this point, I just like to welcome our four speakers Nora Garza, Sarah Bowman, Jeremiah Trinidad Christensen and Florence Henderson and I'm particularly delighted to have Florence back. Many of you will know her work from when she was at internet to she will also know her earlier work from when she was at IBM. But she is now the executive director of the Northeast big data innovation hub, based at Columbia. And one of the things that I hope will cover at least a little bit today is the role of the big data innovation hubs, which I don't think is this widely known among the CNI community is it probably should be. This is an important strategic NSF initiative that actually touches on lots and lots of different projects. The primary focus though of this presentation is going to be the very rapid creation of the covert information commons which has been a sort of an amazing collaborative effort. Like so many others that was born out of necessity as we've tried to respond to the challenges of the pandemic. And with that, I believe Florence is going to start off the presentation. So I will just thank our speakers one more time and hand it over to Florence. Thank you very much Cliff and it's wonderful to be back with this CNI team I participated before as you mentioned and it's always a pleasure. Thank you very much for having us today. Today we're going to be talking about the covert information commons and I'm joined by two of the covert research principal investigators that have been awarded by NSF with covert rapid grants and Jeremiah who leads our NSF studies team at the Columbia University. So the covert information commons, it serves as an open resource to explore NSF funded research addressing the covert 19 pandemic. And that was particularly what we were asked to do we received this covert rapid grant. It is, it is funded by the NSF convergence accelerator program which is one of the newer programs, which provides for multi disciplinary and interdisciplinary research across multiple of the domains that NSF funds. Next chart please. First I'll give you a quick overview of the kick as we call it the covert information commons, then Jeremiah will take us through the library's role and we thought this was a great example of partnership between researchers information technology that big data hubs and the libraries. And two of our principal investigators that have spoken on our kick community lightning talks are going to give their perspective and actually do their lightning talks. So you can see the range of research that's available through the covert info commons and the community. Then I'm going to take you through a quick run through of the search and discovery mechanisms we have in the covert info commons, and then we'll do our q amp a which will all participate in next chart please. As I mentioned, the Northeast big data hub which I'm the executive director for is one of four big data hubs across the US that are funded by the National Science Foundation beginning in 2015, and then we got our next awards in 2019. So I lead the Northeast big data innovation hub at Columbia University representing the Northeast US, the South big data hub, the West big data hub in the Midwest big data hub similarly, our headquartered a couple of universities and then serve their entire region. And this covert information commons was a collaborative project across the four big data hubs. And that's part of what we're trying to do more and more as hold hands and work together to support our community around the challenges and opportunities and big data. Next chart please. So a little bit of an overview. So the covert information commons was created to increase accessibility to information something we all care about in CNI. And this is specifically regarding the NSF covert rapid research awards, which are rapid response research grants so when there's an emergency, they create these rapid awards it could be for floods fires pandemics things like that. So that's what it was funded for initially. It now includes information for additional NSF grants that have covert as a program name or program reference code for those of you who know the innards of NSF parlance, and it facilitates knowledge sharing and collaboration across these covert research efforts. It really started as you know just give access to all this rapid information in one place, and it's evolved so much as a portal as an international collaboration mechanism which you'll hear from Sarah, as well as a community of collaboration which you'll hear more about from Nora and from Sarah. So it's a resource for anybody around the planet as you can tell by the international nature. And then we have researchers students decision makers from academia government not for profits everywhere really working together to leverage these research findings and to accelerate research to mitigate the societal impacts of the pandemic. We organize relevant information in multiple ways if you could just go back one chart. So it allows us to look by research topic institution geography, and we recently on October 30th launched an upgrade that links to PI and research team data next chart please. So this is a quick timeline. We were given the award we were awarded in May of 2020, and we launched to search mechanisms a simple search mechanism into the NSF database as well as this cool machine learning generated map capability. And then in phase two which we launched between July and in October because we had the first launch in July, we delivered more search mechanisms, we have, we have a slack channel, we have a number of other deliverables, we have over 40 data sets that are actually linked to for around the world, as well as these monthly COVID info commons webinars kick webinars. And we actually did this PI survey to get more data that we've included in in the database which is very valuable and now we're looking at next steps and we're going to talk more about that next chart please. So, COVID info commons is a portal, and it's a community. So, in the community what we've done is when we first we're going to do the launch webinar in July we said you know maybe we can ask a couple of PI's to present, and I actually begged one of them because I didn't know if anyone would say yes. Then before we knew it 40 more people offered. So we said oh my goodness. I guess they really want to do this so in the launch webinar in July. They said well do your enthusiasm you're now part of the COVID info commons community and we'll have monthly webinars until we get through all these lightning talks. And now we're scheduled at least through March. So we have hundreds of people in the, in the community and you can sign up for it by going to if COVID info commons.net which is our website. We have three members in our Slack channel on these monthly calls and Nora graciously in our September call when she presented she said this so we bottled it and we have it all our presentations now I'm sure she'll talk more about it but she said your site and the ability to come together is marvelous I thank you especially for thinking about this and bringing us together and people will be able to use your kick site as a proper safe true information source and that's really what we were hoping for. So as I say as an Italian I nearly cried when she said that but we're so delighted that was the feeling on next chart please and this is a totally open community. This is just quickly to see that and you can have these charts I put him into the drop box that these are an example of some of the researchers that presented Nora was in the first one you can see the range here from UT Arlington to Princeton to University of Southern California all different directorates next chart please. And then in October Sarah is presenting today presented. And it was interesting because during her talk there was someone I saw asked a question I'm like oh Sarah Peter asked a question because I have like three others I'm answering right now there was so much real real time collaboration it was so exciting. Next chart please. And then our next webinar is this Friday or tomorrow November 13. And you're welcome to join that as well just go to COVID info commons.net and register under events next chart please. So since we launched as a portal to find rapid grants. We also created a couple of other things one is we created a tab called meet the researchers really trying to humanize this so we have human to human collaboration going on. And so what we've done is we've taken the separate lightning talks from each of the PIs that have done them. And we've put them in separately. We also connect them to their PI profile which we'll talk about next chart please. We've also started doing interviews with some of the researchers to ask them five questions about why are you doing this research and what have you found what are you hoping to find what collaboration opportunities do you have. Once again humanizing it and creating the ability for people to find each other to enable each other's research and collaborate together. Next chart please. So these are a few other resources on the kick website and you can go at any time you could be playing with it and another screen right now if you want COVID info commons.net. So we have under our resources we have webinar videos including a user tutorial for the lingo 4g machine learning Explorer map that I'll go through a little bit more than we have the July September October and we'll add the videos as they come about next chart please. We also have research funding opportunities around the globe US and international next chart please. We also have over 40 data sets from six continents how cool is that people send them to us because they know we have this portal next chart please. We also have groups and guides organizations and networks guides and references next chart please. All of that is provided by people who say hey you know include us and we're delighted so if you know of COVID related resources, feel free to go to coven info commons.net, and we actually have a form that you can fill in to provide data. Under the events we have prior events you can see past events, we have the upcoming events, we have these monthly webinars because we have so many cool lightning talks lined up next chart please. And this is our project team so Jeanette wing is our PI at Columbia University she leads the data science Institute I work in her team. And then my other three colleagues that lead the other big data hubs meredith Lee for the West big data hub, john McMullen for the Midwest, and we're not a Rawlings goss for the south hub. Katie nom is our operations manager Helen Yang is one of our wonderful students she's a junior at Columbia and she's done a lot of the updates to the PI database. We're working with Columbia University libraries team and it teams, and all together we were able to pull this off which is kind of incredible. And we're really delighted about it and now we're looking at our next steps next chart please. So now I'd like to hand it over to Jeremiah. So he can talk about the library's role and they were really important for us because they help us see some of the information science aspects that aren't as obvious to some of the rest of us. So Jeremiah. Thank you Florence. So this is this was a pretty good project to be working on it was at the time. Something working with something that did not fully exist yet. So a lot of the words were in the same process as we were. And a lot of room to kind of think and explore and put it understand what are the possibilities of what this could be other libraries team that we that we have elicited here. All of us are within the digital scholarship unit within Columbia University libraries, all of us have some level of expertise with institutional repositories, working with data metadata data management, even building web applications. So the role that that we were asked to do is was to to be able to come in, explore and think of ways for discovery for the information that that that was there. We did know from the beginning that that we should be thinking very flexible that this is something that, although it was, it was geared toward the rapid awards there was room for growth in here. And in all of our thinking we needed to keep that in mind that it wasn't just for this information alone. So, as part of that we needed to understand what what data was available from NSF. We need to also think through what are the additional information that might enhance in the data that's available through NSF that might be helpful. So we took a look at the existing data. The data came through both API and XML. We took a close look at what was available there. The XML actually had a lot more information there, but it wasn't updated as as as quickly. Whereas the the information from the API was very regularly updated. So we compared we we assess for the completeness we we tried to understand any errors or any problems that might might be there that might exist. So we mapped out the various fields or different relationships thought through how each one might help somebody explore if they if they saw it readily in the in the front or in the back or what all those relationships. We also took a look at additional information that we might be able to pull in to enhance this we we thought through, you know, if we brought in connected to to information out of orchid what would that look like. So to other other research that each individual researcher was had had already done or will do in the future, making connections to wiki data for instance for the institutions or other information that might be pulled out of there, out of like the abstracts and be able to findable and showable. The same thing with geonames whether or not we wanted to explore looking at building interactive map that that like lets people make selections custom selections based off of whatever area they wanted to draw on the map. All of these kind of things we are looking at same thing with controlled vocabularies to. So one of the PIs we knew that the survey was going to go out so we needed to think through what are the what's the information we wanted to get that might be really helpful and the orchid IDs were one of those to make sure that we connected the individuals that were listed there with who they are. Same thing with with trying to understand any unstructured keywords that might happen. So how do the researchers see their own work how do they describe their own work what are the keywords they apply to the to to that to that research and also the output to since with this was so early on it. Most of the there were no outputs at the time when we were looking at this. So really had to get an understanding of what we were looking at and how to display that through the site. So after we got roughly about 100 or so responses from the survey, the library team took a to the dive into the data and just focus very specifically on the, any of the websites that were that were named in there as well as what type of output was going to be We did a kind of cursory pass of this to try and kind of organize ourselves so this is the this these couple charts are show you kind of what we were thinking at the moment. So with the URLs. You're all in there that some of these were associated with universities somewhere.org comms somewhere in GitHub, we pull up the Google ones just because since these are. These are things to exist. They did not exist at the moment. All sites were were might be associated with with log information from before from a particular institution or just from a few individuals so we knew we might have to look at these later but we needed to understand how many of these we we were that we had at that We also wanted to to look at the output itself and there was such an amazing variety of output, everything from data sets to to things that would exist in social media that that might be a blog that might be a video that might be some kind of a conference or some kind of Well, just about anything you could think of it was it was quite expansive and really really interesting to see. And with all of that, we needed to keep in mind that, once again, this had to be something that is simple that is clean, fast and flexible, something that whatever is built off of this it could be the building blocks for for for the future for whatever it's going to become. Also something very lightweight that that if we if we built something very complex in the back end, we may be stuck with that particular back end. We put the pieces in place for being able to expand on this one. And this is this is what you see right here so everything from the the keywords on here that that link out and make connections. But that's it for the library side of it. So I'm going to pass it on now. Thank you very much, Jeremiah, we really appreciate it and your partnership has been great. So now I'd like to pass it over to our our PIs, the COVID rapid PIs that are joining us today they're going to do their lightning talks and give us a little perspective on this coven info comments. There's first will be there, Dr Sarah E J Bowman associate research professor in the Department of Biochemistry at the University of Buffalo, and then Dr Nora Garza from Laredo College in Texas. Sarah take it away Jeremiah next chart please. Okay, great. So can you all hear me. Yes. Alright, so I'm going to tell you a little bit about some of the work that we're doing in the lab that I direct. I actually run a crystallization Center for structural biology at Hotman Woodward Medical Research Institute, which is a small nonprofit research institute and I'm also affiliated with University of Buffalo. Go ahead and next slide. I'm a structural biologist we do structural biology and what that, what that means and what that has to do with the COVID-19 pandemic is that what we're trying to kind of figure out is what all of the different pieces and parts of the SARS cove to virus really look like. So many of you are probably familiar with this type of figure which looks large and scary and it's got these big red spikes on it well that spike is actually a protein. It's on the surface of the SARS cold to variant, and it's the protein that interacts with human proteins. And so what we do in structural biology is try to understand what these things actually look like because it helps us to figure out how to design vaccines and drugs and so on and so forth. So go on to the next slide. Next slide. Can you guys still hear me. Okay, great. Let's go back one. Sorry. So structural biology is actually somewhat difficult to do and in part this is because the things that we study are incredibly small. And so I like to give people a framework for how, how big we're actually looking at so if you, if you think about the width of a human hair which is obviously out not not quite to scale here, but what then we ramp down to a single grain of pollen to one red blood cell to an aerosol droplet all the way down to that little pencil mark of a SARS cold to variant. Now what we're trying to do in my lab is actually look at what do these things look like in the pieces and parts and so if, if the small thing is is so small the pieces and parts are even smaller. There's a number of techniques that let us do this go ahead and go to the next slide. I'm sorry there. So don't worry we're not going to actually talk about the SARS code to genome, but we do think about the SARS code to genome in terms of what are the different proteins that are encoded by it because those are the things that we're trying to understand what they look like. So the structural biology community and the whole scientific community as all of you are probably aware of have really worked together, you know, very quickly to try to understand a lot of these things. And if you go to the next slide the reason again we're trying to do that is because these are the proteins that actually are treatment targets. So if we can actually develop drugs that will bind to some of these proteins to stop them from acting, we can stop the virus if we can develop vaccines for the that that bind the spike protein and that's actually part of what has been really great in the news lately is is is some of this work right. So in structural biology there's a couple of techniques go ahead to the next slide. The one we work on in in the lab I work with is x-ray crystallography. The only thing you need to take out of this is that what we need to do x-ray crystallography is to take our sample and turn it into a crystal, and that turns out to be the bottleneck in the whole technique. And so, next slide. So what we do in the crystallization center is we actually facilitate that happening. And the way we do that is in a high throughput way using a 1536 well tray that is essentially the equivalent of what most labs do in a 96 well tray so it's about 16 times that. And we have really extensive robotics, imaging instruments and a lot of expertise because we've been running for about 20 years. Next slide. And so we were we received our NSF rapid to essentially facilitate researchers from all over the country who are doing SARS-CoV-2 research to try to understand the protein structures so they could send their samples to us. And then they could proceed with with their studies. So this is especially been important in the SARS-CoV-2 kind of situation because so many labs are running at really decreased capacity because of for safety, right. So, go ahead next slide. So we've really become a major resource for we've been a major resource for structural biology but it's been a really fascinating time to be doing this work with the SARS-CoV-2 proteins. So this is kind of some of the types of images that you get is to actually view the view the wells over time and you can see crystals forming sometimes they look like little flowers like in this one. We have multiple imaging techniques that help us to really determine those are crystals. One of the tricky things about this is so that we have 1536 conditions, we have a lot of different imaging over multiple types of images and so we end up with about 14,000 images for each experiment. And at the moment right now people have to look through all of those. So one of the things our SARS are our rapid funding funded. Next slide. Was some great work by this fantastic post back student Ethan who essentially developed a graphical user interface to make it a lot easier to actually view these view these images and and be able to really interact and work with the data, and he incorporated a really recent machine learning algorithm that identifies these crystal images so we've submitted a paper describing the software it's on GitHub and so available for people and he recently also got a poster prize for for this. So next slide. We also had just some tremendous success screening for SARS code to samples for users so these are some of the examples in the top corner you see some crystals of one of the proteases which is a main drug target in complex with a number of different inhibitors. The little heart shaped picture that's actually the main protease the crystal structure that was solved by our collaborators at Oak Ridge National Laboratory and again these flower like crystals appear within, you know, a week. And then we've, we've actually just this week gotten some amazing results on one of the proteins that nobody else has actually had any structure structural work with so we don't have I didn't have any time I wasn't able to put pictures up about that but it's really exciting so next next slide. So it's, it's been really great to be part of the coven information Commons as well, because it really facilitates connections and I think one of the really cool things about this is that, you know, there's a lot of people who are doing a lot of different type of work so we do structural biology. There's people who are studying all kinds of different things. And already I can say that that I have active stars collaborations. So this is one of the things that I think Florence will be talking about a little bit, but essentially you can do searches with keywords to see who else is working on things that are kind of near you. And when I did a search earlier this week on on this, I discovered, oh I've got, I actually have three active collaborations on stars go to projects right in my little network already and so it kind of gives you an indication of how well this this network is working. And then also, next slide. I also just last week was contacted by somebody who found our work via the coven information Commons website, and it's a graduate student in South Korea, who is doing research on how data open access during the coven 19 pandemic kind of compares to other types of things. Your advisor is in the UK. And so we're actually scheduling a time to talk about about how love this stuff works. And so I think that one of the, you know, exciting parts about what's happening right now is all of the connection that that is happening and that's you know, that is being enabled by by kind of what's happening with with the coven information comments so last slide is just my thank yous. So I'll, I do want to mention there is a slack channel for the coven information Commons and we do have a special structural biology channel on that. So if you're interested in any of the things that we're doing you can come and chat chat with me there, or check out our website or our Twitter handle. So, thank you. That was marvelous thank you so much for enunciating the international value how cool is that that you. That is just so cool and I have to say that Sarah actually you created the structural biology channel within the slack channel didn't actually one of my one of my co workers that hw I did. So that's what who also has a rapid award so. And so that's a really good point that this is all community driven so the community is finding this the international community is finding it. You can create your own like sub channels within the slack channel to enable collaboration for your focus areas. So we're really trying to make this as community driven as as possible. And we're so grateful that the community is getting benefit out of it so thank you for sharing your story Sarah and being part of the community. Thanks for having me. Of course, hang on we're not done. And so Nora, I'd like to pass it over to you and as I mentioned Nora was one of our first stars in the coven information commons community webinars and her, her co p I Gabriella is on the line listening in as well and I'm sure she'll want to thank her she's been wonderful to work with. So Nora take it away. Thank you so much and our and our most recent meeting with our students and our faculty. Last week they said well thank you to Dr. Godson I said no thank you to the National Science Foundation for funding us, and a big thank you to the information commons which has made it even more exciting for us, and for our students. So our project is to do multi generational. And so my star is Gabriella Solis Cavazos, who is a former student here at Laredo College and now she is our undergraduate research coordinator. So our project is using the cove it data that's already there in the city of Laredo website to teach quantitative reasoning skills to undergraduate Hispanic students. Right on the border with Mexico, the US came to us. So we've been here a long time, and our college is about 96% Hispanic students. Can you change the slide please. So, we are working with six faculty members. Of course, you cannot say it enough. There's a huge stress on all undergraduate students particularly those pursuing a STEM career. These are hands on kind of students. They're very high energy. And with a cove it you know there were interruptions and their undergraduate research opportunities, they would do bird counts they would work with simulators they would work with their faculty, and all of that is gone for now. We're very safe, but we are not meeting face to face. So it's important to retain these students and their classes, even if we have to do it virtually so we're going to be using that can you change the slide please. So when we first got the opportunity to actually write the grant, and I've mentioned it to Florence, I had read her Rory and was reading he, the heat brothers, and somewhere there they mentioned something about when the impossible happens, think of the possible. I like to be on the positive side of everything. I wanted to set up and implement a data analysis research experience to improve quantitative reasoning. Thanks to NSF and our meeting for the HSI project principal investigators. We met I met Dr. Esther Wilder from New York, and she's been amazing. And she and I in conversation said, you know when students can really use the terminology practice it, you know, everyone is impressed one they get to practice their vocabulary, and they and understanding and using quantitative reasoning is really important. So we wanted to create a professional development opportunity for our faculty and any quantitative reasoning, and then we wanted to evaluate the project's impact on our students quantitative reasoning so we have faculty from chemistry for math from psychology and from nursing, and they're working with at 14 students, and all their projects are related to the co vid. So if you'll change the slide please. This is what they used to do before. Obviously they're not out on the river together. Dr. Meng and his students and and soon to be Dr. Selena Martinez, who I think is at University of Buffalo faculty development training. We are reading a book called math for life. And we're using it with our students or one of our other teachers is a teacher statistics so this is great. Change the slide please. You can see there the Laredo City of Laredo dashboard and excuse me, we are. These are the posters that are used to students used to prepare before they're going to be preparing them again for the projects that they're doing changes slide please. So you have there that we're using the book. We have our faculty. We had this is a second or third meeting that we have had with the students. And so this past week, two of our math instructors Dr. Carranza and Mr. Carranza brothers, both math teachers. They talked to them about the why intersect and what we were trying to get was, what was the rate of change we wanted for co vid, and that would be to be zero to flatten the curve. So they're talking about these things, graphing and charting and it's, it's exciting for our students. We are keeping them motivated and that was my big big worry. I don't know about other universities but a college a community college with stem students and the situation that we're all living, you know we have had many students drop out, because they have to do other things to take care of their families. Next slide. Ah, co vid information commons community welcomed us. We became a part of them, and our students and our faculty are using everything that's there. And even to write future grants, we are connecting there connecting the dots. It is easy to access. You can find out any other person that's working on the rapid grants for men as if you can find it there. The research research runs the gamut from research on the actual virus like the one that you just saw to the educator and the students to co vid, you know what has been happening so there's all kinds of things that you can access there. A quick perusal will put you in touch with information on informal science learning during these trying times. And of course, Gabriella Solis, who is working hand in hand with our students 10 hours a week, each student works 10 at least 10 hours a week on their research, and being able to connect to the information is very exciting. I know they're all looking forward to tomorrow's lightning, lightning talks. Next. And of course, if you have any information on any would like any more information, you can contact me at nrgarza at orado.edu, but you can find more information of course on the on the information comments. Thank you very much. Thank you Nora and thank you Gabby who has been in the background and foreground of this whole thing to so that was wonderful both of you are just so inspiring and this is an example of what the coven info comments is bringing together we're very fortunate. Next chart please. So now I'm going to give you a quick run through of some of the search and discovery mechanisms that our colleagues have been talking about next chart. So as we mentioned when we first launched this in July or a minimal viable product, if we got the award in May, we actually launched it with two search mechanisms the two that you see in the pink boxes on the bottom. So the COVID research explore machine learning generated maps, as well as a customized search by NSF directorate and what we try to do always being community driven as we listen to what people want. So as you could see Sarah was using machine learning generated maps I'll show you more about that so you know where that came from. And then I've had other PIs who have said that they actually like going and hanging out like in one of the directorates just to see what type of you see a nor is not in your head to see what type of research is being funded. And as Nora mentioned you could look for gaps you could look for continuity you could create a constellation out of stars. You know how could this all work together. And so what we try to do is you know provide the different tools that are valuable for different people. And one of the things we're planning on is a student hackathon or challenge that we hope to announce in December to do some of the things nor that you said your students are already doing is have them leverage these different search tools to find opportunities for collaboration to bring things together things that are not being done yet you know what's missing and to give us some feedback on the different search mechanisms to see if we should consolidate it or just keep all three of them there because we've been you know building this over time. And we just announced the NSF covert awards and PI database which is really fun. I'll take you through all of these next chart please. So the one that we just launched as as Jeremiah was talking about to is the awards and PI database and as you can see, when you go into it, you can get a list of all the awards you can do it alphabetically by PI by institution, and then all these things are integrated, all the different filters you can use to actually search the awards. You could look by NSF directorate by division by the institution themselves like Laredo College University Buffalo, the state that they're in our territory we have Puerto Rico in there as well. By region the four big data hub regions PI name the program officer NSF the program name reference codes all sorts of stuff so that you can actually go in and zero in on what you're looking for next chart please. So when you go into this you can click on one of those awards and you can see all this information about the award we were talking about. And the really exciting part is you can also click on you can see how the name of the PI is highlighted so we'll click on Peter rose next chart please. And you can see this is his PI profile and so Nora has one Sarah has one, and he sent us a fair amount of information we did the first PI survey. You can see his institution his email his orchid ID which is a hot link to his orchid page so you can see what else he's had going on the websites he provides that either have results of his research or related to his research. Once they do a PI lightning talk we add that here as well so those are also in Sarah's and Nora's. And then we not only have the list of rapid awards some of them have more than one rapid coven award, but we have what do they expect their scientific outputs to be the outputs from their research collaboration opportunities and the keywords they use to describe what they do, because it could be that there are keywords you know based on the NSF database but I always like to ask people how do you classify it you know what is it that you're doing, because it is looking at things. Next chart please. So that's the PI database and you could go in and use it as much as you like. The next one. This is a very simple thing, but actually the machine learning maps I'll do first and I'll do this simple one at the bottom so these are the two first mechanisms, the machine learning maps for the recent current research explorer and then the NSF directorate version so next chart please. The machine learning generated maps are based on a tool called lingo for G Explorer, and when you go into it you can actually see a topographical map of the NSF rapid awards, and the topical areas so this is interesting and if you look at it and you can see what is an example protein at the bottom, that looks like a big mountain so there's a lot going on down there or social distancing or surveys or academic learning. And so you could go in and you can zoom in on this and you can also click on any of these little dots, and actually learn more about that research and whenever you click on any of the awards as well as in the next page, you get this information on the right and you can choose what fields you want the name of the award. The institution, the PI name the amount of money they receive their email address the state they're in, you know the program officer there's all sorts of information you can pull up based on what filter you want to use next chart please. Then there's also this view and this is like the one that Sarah showed where these are the tree maps versus the topographical map next chart please. And as you go into this you can actually customize the view you have so you can see here we can say I want to color this by this cute little cog up here, you can color by state. I'd like to size it by the amount to see how much money they got. A lot of the rapids are about 200k so it's pretty consistent labeled by institution and I want to highlight the same color and let's see what that does for us next chart please. So here I click on one of the institutions in California so since I said I want to see it by state I was a color state by color and highlight everything the same color when I click on a California institution. All the California rapid PI rapid grants come up how interesting is that. So if you want to see who else is in your system, or in your state this is a way to do that next chart please. You can also look at awards, you know by institution and in California. So this would be I asked for the view by institution and over here in the query I can say and state California so it shows me just California last page. They colored in the California ones but there was a whole bunch of them. Here I see just the ones in California. Next chart please. And so now I can customize the view by PI so I look on I label it PI next chart please. I can actually go in and go for a particular area so I could say end machine learning or end machine and learning. The joy of bullying in this case is you get to try it different ways and see what you get. So, you know the joy of programming for those have been in it. And so these are all of the ones that are related to machine learning next chart please. And then I could look at you know the PI is that are doing the machine learning, and then next chart please. So the other search mechanism, which would be the NSF directorate so here you can click on these icons to find the COVID rapid grants by NSF directorate. The directorates biological sciences computer and information science engineering called size often education human resources engineering geosciences, mathematical and physical sciences social behavioral sciences and the director and they all represent COVID rapid grants. So if I click on office of the director next chart. You can see this is actually our COVID info commons grant. So it shows you a list of all those in the directorate and then you can click on this one next chart please. And it gives you similar information as to what we have in the PI and awards database we've integrated this with the PI information, including the abstract and a lot of useful information next chart please. So this is a quick overview of the COVID information commons, the portal, the community, the PI is that are working in it, the libraries team and how they enabled us to put the information together, the best way that we could to gather the right information from the PI is to make it more useful. And one of the things that we do is really try to follow the fair principles, you know for data in the COVID information commons, making it findable accessible interoperable and usable. So actually we're going to be presenting at the code data go fair conference, virtually in Paris only virtually, November 30, and we have nine of our PI is presenting there as well. So findable is what stands for so you can find all the NSF rapid awards and we've actually added egress and SBIR is this TTR is for those of you who know some of that nomenclature, and we're looking to add more over the next year. And you can find them, you know, easily in context. It's very accessible people are using it around the planet and you can get all these different elements that we talked about interoperable from a researcher perspective, we're actually thinking about the next stage of the COVID info commons and Jeremiah and I were talking about it earlier today, but we're thinking of how cool would it be we haven't figured out how to do this yet. We could actually crawl the data and help Sarah use her data to find other data in the same type of area, or for Nora and her students to be able to have their data, find data of like what how is it happening in other states and other HSI's, you know how does this compare how cool would that be. So we don't know who can help us code it but we have it, we have a dollar and a dream. And so if anyone knows anyone who can help us with that Jeremiah saying we're open, you know, to that, and the data is already usable you can download it from the Explorer tool from the PI word database. You know there's a CSV file with all the information, and as well you can download in, you know, Excel or XML all sorts of formats from the directorate level information, and we're looking to make that even more useful into the future next chart please. So the next steps use it, you know it's your tax dollars at work you may as well use it. Your kids can use it your grandmother can use it your friends in Italy, anyone can use it. Join the coven info commons slack channel if you like it's on the kick website if you go to the coven info commons net there's a thing at the bottom that says join the slack channel. You can sign up for the community and the kick kick events there's one tomorrow, there's the bitly for it, and you know help us have researchers collaborate with each other and with students. We're doing the International Fair Convergence Symposium as I mentioned at the end of November 1300 to 1500 UTC, which is now 10 to 12 Eastern it was 11 to one when they sent me the invitation so luckily we figured that out before November 30. Because time clocks changed but UTC doesn't, and you can email us your input or questions that info at coven info commons net. Next chart. So now we have time for Q&A, and we'd be happy to take any of your questions or your answers we accept both as I usually say. So if there any comments I don't know if there's anything in the chat Clifford Diane. Thanks Florence. Thank you so much. Well, first of all, thank you for that tremendous presentation. All of you. This is very exciting tool and just really remarkable the kinds of synergies that can occur when you bring different sectors of the community together with their various needs and interests. It was really exciting so thank you for this wonderful demonstration of the power of that. And my apologies to everyone I'm having some technical difficulties so you'll just get my pretty flower for now and not my pretty face. So that would like to invite our attendees to share with us any comments or questions you might have I think Florence and Jeremiah throughout a challenge there so if we have anyone in attendance who would like to take them up on that offer please let us know. And I'll just ask while we're waiting for attendees to weigh in. I'm just wondering what kind of outreach are you doing to, you know, who, who, who are you hoping to draw in how are you reaching them, other than coming to speak at CNI of course. What, how have you been reaching people. So I, we try to do it early and often. So, we presented at the academic data science Alliance leadership summit, which was about a month ago I think, and so that was a really good discussion. I presented this recently at the, the UC Santa Barbara responsible machine learning summit. So people are reaching out which is wonderful. You know, as I mentioned we'll be speaking at the international go fair conference later this month and I think that's a great opportunity to increase the collaboration across the world. I'm hoping that out of that we have more researchers that start using it on purpose like the ones that you know communicating with Sarah. I hope that we find more data sets maybe award databases, you know that we could look at and try to link to, you know this isn't even though we're called the big data hubs where data innovation hubs we don't have like big data, you know, storage networks and things like that where we link to everything. We're not like a collaboration hub, but those are some of the things that we're doing we're going to be reaching out to all the 990 PIs that are in the new awards database with the PI information to get more of them to give us their survey data so we can enrich it more. We're very, we're very lucky that some of the PIs actually did fill out the survey before they could see what we were going to do with the data but now that they can see I'm hoping that some more of them say oh yeah this is a great idea. This was in 1970 that you know that listened and just gave it to us which we're really grateful for when we asked for it. So we're reaching out as much as we can I've spoken on the international coven research seminar that occurs monthly that's led by the Pittsburgh supercomputer Center with collaborators from around the world. And we've also just started to talking with the coven 19 HPC consortium, which works with exceed and price in the US and in the EU. So we're going wide you know we want as many people to use this as possible I like to say this is your tax dollars at work so use it, and help us to help the research move forward because there's a lot to figure out. You know when I started this project, NSF actually came to us in March and said hey we're thinking of this thing and I was like wow what's that about, you know we started talking about it. And then we actually at the time did a quick, a quick lit review so to speak, and I found that in 1985 NSF funded. I think it was the second biennial coronavirus workshop in California. I know right and I'm like where's that data so there's actually a springer publication with some of the information in 1986. And as a researcher I look at that and go man, what's the same. What's different. What did somebody know, and I actually reached out I tried to find one of the PIs, but they were probably my age back then but one of them was related to one of the California PIs and they were in touch with him as a retiree and I asked them to send him a note to see if I could interview him or something and I haven't heard back yet but now that we have come so far with this I'm going to see if maybe he'll talk to us how cool would that be. You know that we can share but some of the things are consistent you know you read it says wash your hands you know social distance this was in 1986 so this is not rocket science as we say, but some of it is obviously based on what Sarah was showing us but you know there's a lot that we need to learn from the past and predict the future so that we can try not to repeat it the same way I think. Does that answer your question. Yes, thank you very much. I think Clifford would like to ask a question else I'll hand it over to him. Just jump in while everybody's thinking about questions. First off it's wonderful that you've made that connection into the code data go fair thing I think I've had my eye on that meeting I think it's going to be very timely, particularly in the kind of context you're putting it in. So the question I had and I asked this a little timidly not wanting to create any problems is, I noticed on a couple of your of the slides you had illusions to various NIH programs as well and clearly in it while NSF is one of the major funders in this area in the US NIH certainly is as well. Can you say a little bit about how you're coordinating with or thinking about, you know, how what you're doing connects and relates to the funding that NIH is doing in this area. Absolutely. And as our PI Jeanette wing likes to say if you think of COVID you think of NIH, you know, because that's for all the medical and healthcare professionals are. So we, we really want to partner with them. When we first got this award it was so much to do in a short period of time we had to keep our head down and just do the COVID rapid grants which is what we were told to do. We're going more broad across NSF but one of our next goals is to think about how we can work with NIH and maybe DOE, you know, because even Sarah was talking about the R&L work that you know that she's working with them. And the HP the COVID HPC consortium works with a lot of those different groups so by collaborating with them we're hoping we get into their projects. So the, the CUIT leader Carl Ragnoth and I spent some time last week crawling the NIH website and looking at their search mechanisms and they don't have an API but they do have award databases that they download and they update every week. And so now that we're done with all getting this all out the door and our last October 30 was our last refresh with the new PI database. Now we're going to start looking at how can we bring some of that other data in. You know in the open public information should be fine but we do want to open more doors into NIH to start talking about what could we do together, especially if we're thinking about, you know crawling and searching data and metadata. You know open science data maybe not HIPAA compliant data you know we have to decide what data we're talking about. But I think it would be wonderful to do that more with them so we do plan on talking more to NIH NSF is offered to connect us. And I have some contacts there actually tomorrow we have the computational approaches for cancer workshop I'm on the program committee for it as see virtually, and that's working with NCI. So we are, we do plan on talking more with them, because we think we can help these collaborate these researchers collaborate more and make more progress. So thank you for asking that cliff. And any friends and family of NIH that wants to talk to us about this I'm very we're very open and collaborative as you can tell. I'm sure you've met Patty Brennan at NLM at some point. I haven't but somebody mentioned we should talk to him. You should definitely talk to Patricia Brennan, the director at NLM she. I think she'd be thrilled to talk with you about this. And if you would like to create you know like a little letter if she knows you if you send an email I don't know if you can I don't want to put you on the spot but that would be happy to do that after that. Great, thank you very much very thoughtful of you I'd love to get that. Now we're ready to start talking, you know, past like the heads down you know let's get this done approach so this is perfect timing thank you cliff. Sure. And I don't want to take over Sarah and Nora, I mean Sarah you may be working with some NIH researchers already. I mean, so a number of our researchers are NIH funded. And I think that I heard, I think maybe earlier this week I was on a meeting that was indicating that part of why a lot of even the more biomedically relevant funding for the rapid went through NSF was in part because the NSF is really equipped to be able to provide funding that quickly with the rapid award and that the NIH didn't necessarily have have those mechanisms in place and so I do think that there's going to be more NIH funding, kind of available and. And so, but it's just a longer process for NIH so. Interesting, thank you for sharing that. Yeah, and I may know some people who might be interested in so I'll touch base with you kind of offline so. Thank you Sarah that would be great yeah all friends and family welcome you know new friends are making a lot of new friends with this whole effort so that would be wonderful thank you. I'll answer questions Jeremiah did you want to add anything to that. And he may be having connectivity issues we're all sharing connectivity internet and our computer blew up issues today so it's really a miracle we got this done so thank you team. There do seem to be goblins in the machine today. I'm so glad you overcame them and joined us this has been a wonderful presentation and I'm really glad that our community had an opportunity to get caught up on this great development. We need to we need to thank NSF, and Chaitan body was going to try to join us today but he had another meeting he's our program officer for this, and I have to thank Martin Halbert for mentioning we should think about presenting. You know from the hubs, you know on on this so I have to thank Martin as well. Yes, Martin is here in the audience. Thank you Martin. Well I'm not seeing any more questions and we are at time. You're right on time thank you so much for having us and thank you Nora Sarah and Jeremiah for your teamwork. We really appreciate it. Thank you for having me. Thank you. Thanks. Yes, thank you. Thank you all for for coming I'm sorry Cliff go right ahead. I was just thanking them as well. Thanks. I think we'll do what we have been doing which is go ahead and end the recording at this point, and thank all of our attendees for joining us and any attendees who want to stay with us here. Please feel free to stay on after we turn off the recording just raise your hand we can unmute you, and you can approach the podium and have a chat with our speakers. That I will be ending the recording and thanking you all and wishing you a lovely evening. Good night. Thank you. Good night. Thank you. Good night. Bye bye.