Welcome everyone, we're gonna go ahead and get started. Hello, welcome to the last session of the day. I am Paige Pope, I am C&I's communications coordinator. My first one, of course, thank you for being with us today. It's been a great first day and I'm looking forward to seeing familiar faces and meeting some new people after this session at the reception. So we are going to dive into the lightning round. So these will be five minute back to back sessions. Very, very exciting. The heart will be racing. I thank our speakers for joining us here as well. Please hold your applause between each speaker if you can. I know it's hard not to. And then also questions, please hold those until after at the reception. So following these five minute lightning sessions, my colleague Diane's going to come up and explain our breakfast discussion tables, which will take place tomorrow. And then they'll also be brief one minute roundups of those topics from some of the facilitators. So once the five minute lightning rounds are done, I ask that you please wait to also watch that as well. So without further ado, I'm going to hand it over to Christina. Thank you. Hey everyone, I'm Christina Drummond. It's a pleasure to kick us off here with a quick update on a suite of research projects. These are all aimed at strengthening our cyber infrastructure for the scalable trusted exchange of sensitive usage and impact metrics that have to do with the open access and publicly accessible research that's created in our public and commercial organizations that make up our ecosystem of scholarly communications. This update builds from a workshop that we held prior to the spring C&I this year, funded through the National Science Foundation. And at that workshop, we had the objective of coming together to understand what was needed to scaffold our infrastructure here in the US to support that evidence based decision making that's related to open and public access impact. We brought together experts from publishers, platforms, data infrastructures, standards, alongside program officers to explore the topic, develop recommendations, and I only have five minutes, so I can't tell you all of what they did, but that's why we have QR codes here. So you can grab the proceedings, you can check out the videos that we pulled together. We have a wonder collection of expert invited talks on the state of usage and impact data. Suffice to say, there are multiple research action areas that we identified as necessary to inform this question of whether we need to have national cyber infrastructure. If that open infrastructure should be global, should be federated, and what that means. We explored how we need education and advocacy, but what came to the fore is that first of all, we have to have a common vocabulary, we have to understand to collaborate, and from those collaborations, we also need to learn from minimum viable product. So through the support of the National Science Foundation and the Mellon Foundation, we've been able to jumpstart some research to assess the unique aspects of usage and impact exchange for open journals and open data, and how that extends to books, while an infrastructure and MVP is moving forward to look at how best to exchange book usage data. An NSF Eager is supporting team at Stratos to document and crosswalk usage and impact data vocabularies. We commonly refer to as the fruit basket of metrics. So thinking about how we compare apples to apples to oranges to papaya, so on, so on. We need to understand that vocabulary and have those crosswalks in hand. The same award is also supporting some folks over at Clark and Esposito to analyze and document the usage data supply chains. So we understand how that data from the point of creation when folks access these digital research objects and scholarship outputs, all the way to the point where we need it for reporting in analytics and evidence-based decision-making. And we have that for open access books. You see the bitly up there, but we also understand how does that particular supply chain differ for publicly accessible journal outputs and open data. In the meantime, the Global Open Access Book usage data trust effort, I'm the executive director of, it continues to move forward with some research and development. So with the support of the Mellon Foundation, we've started our technical development to complement ongoing governance and policy development. We have a team over at 67 Bricks that are in process of assessing the existing operational open science and open scholarly infrastructure environment because we want to build on what is already out there. We need to identify what meets the requirements to play specific roles in a scholarly communications, usage and impact focus data space. And I'll pause and say, if folks have never heard of data space, there's a whole thing called international data spaces, IDS, you guys can Google it, I only have five minutes. But that said, with this particular project, we're working to understand how this established European reference architecture model applies to our scholarly communications ecosystem and which entities that exist today can play some of the five key service roles that are necessary to run a data space, namely identity access management, metadata brokerage, data exchange, clearing house so you can understand how those transactions occur, vocabularies providers of which of course we have many, and data processing app store so we can all go to the same place. Might be able to tell that's a very complex onion. I'm all about the fruits and vegetables, I think I'm getting hungry, I know we have just a couple more minutes to reception but I'll note here that the intent for the book usage data trust and the IDS pilot or the data space pilot isn't to recreate the wheel but rather to bring together and reinforce our existing infrastructures that we use today in our institutions. And so with that, I'll note we're also wrapping up your work to draft rules for the ethical controlled data exchange and data use for that usage data that's coming both from public and commercial entities to those who are receiving it. And so these principles in general are gonna be put forth in a public rule book, you'll see that shortly. As we hand it over to some legal scholars to come up with model contracts. Just in time for a first set of organizations to pilot the data space next year. So if you work with OA book usage data and you want to support the work we're doing, participate in the pilot, learn about data spaces, you'll get to find me at the reception, thanks. All right, good afternoon everybody. My name's Eric Mitchell, I'm the university librarian at UC San Diego and co-PI on leading an IMLS funded three year effort that seeks to develop data science expertise in doctoral students and early career information professionals. I'm here today speaking with you, representing my co-PI's Jane Greenberg, Rachel Frick and John Wheeler, as well as my colleagues in program planning leads Crystal Goldman and Kevin Popovic. And I'm here today to talk with you about our focus on innovating professional education and to invite you to join us in exploring this idea over the next several months. So over the last five years, leading and its precursor project leads have trained dozens of fellows in data science and we're so proud to have these fellows moving on in their careers and excelling in their various roles. We're also really grateful for the dozens of mentor organizations and mentors who have participated and supported these fellows over the last five years. And this December we're graduating our final round of fellows through the support of IMLS and we really are very appreciative of all the work everybody's put in. As we wrap up leading, we're working to publish our data science curriculum, we're working on publishing best practices and sustaining communities of practice and we're looking past leading and asking questions about the future of professional education. So I know we all share a commitment to supporting the next generation of information professionals and I recognize that leading is just one of many a wide range of these education programs. Earlier today I was chatting with a couple library directors and we were sharing best practices about supporting early career professionals and I know a lot of us have been through programs like this. Within leading, we saw a lot of positive career outcomes come from participation of our fellows and we discovered or rediscovered rather that sustainability is one of the key challenges that all of these programs face in our field. And so as we thought about what sustainability means for leading, we also noticed that there were many different practices in place. And so these two issues, excuse me, these two issues, the realization that sustainability is a key problem and that there's many examples of success prompted us to ask this question, how might we help libraries collaborate to innovate professional education that impacts recruitment, growth and retention? As I said, this question has guided the work of leads and leading over the last five years and we think there's an interesting conversation for us to have as a broad information community in this space. So to explore this question, we're gonna be convening a series of online workshops in January and February, 2024, with the goal of bringing us back together around the CNI in San Diego next March to share what we've learned. And we're gonna use a design focused methodology note as the innovation funnel where we'll first learn more about the problem, something we are all familiar with. We'll work on identifying possible solutions and then develop outcomes that we'll then present. So we would love to have you plug in and engage in this work. If you're interested in learning more, I encourage you to sign up on our listserv using the QR code here so we can share information with you as we conduct our planning. Hit the data list dot design site to learn more about where we currently are and come to the birds of the feather breakfast that I'll be hosting tomorrow morning at 7.30. Thank you. Carnegie Gimellon is pleased to announce a soft launch of InGen, the ecosystem for next generation infrastructure. InGen embraces the idea that infrastructure includes people and technology and that the best infrastructure is built when you embed engineering in a human centered ecosystem. It reflects the realization that open source software and open science are drivers of next generation infrastructure. CMU's capacity for both has grown to the point where we feel we can influence national and even global strategy and programs and policies. Having said that, we recognize that no single institution is going to build next generation infrastructure by itself. So InGen is an umbrella framework through which we hope to identify partners both within and beyond CMU. Some of the initial programs and priorities include working on open science and automated science through the CMU Cloud Lab. Cliff mentioned this during his opening remarks today earlier. CMU Cloud Lab is an offsite remote state-of-the-art research facility for life sciences, materials science and engineering research. Any CMU researcher with a computer and an internet connection will be able to access, experiment with and share data, software, a wide array of instrumentation methods, protocols, AI and ML algorithms. Originally developed by Emeril Cloud Lab for Bay Area startup companies we explicitly do not want to share results with each other. We are re-engineering the platform for the broader more open university environment. As an example, I'm glad to note that the Emeril Cloud Lab has open sourced programming language for the platform. Cliff also mentioned that in October at CMU in Pittsburgh with funding from the National Science Foundation we convened a diverse group of participants to examine whether Cloud Lab could be a model or a platform for a national network of these so-called self-driving laboratories. So anyone with an internet connection and a computer will be able to conduct open automated science. Second program is looking at climate change and de-carbonization through the open energy outlook. This is an initiative from the Scott Institute at CMU. It's a model for looking at the impact on de-carbonization from different policy choices. Open source software, the CMU OSPO is working with the team to build a community around its subsequent development and use. On the national stage, the Department of Energy has already expressed its interest in using this particular model. Cliff also mentioned the importance of cybersecurity. The CMU OSPO has been working with the Software Engineering Institute which is a federally funded research and development center and charged by the Department of Defense to be its software advisory group. One potential point of connection with the broader network is I'm in discussions with Amazon Web Services around building a platform for evaluating AI-assisted and AI-generated code. Building on CMU's strengths in computer science, we've been conducting some educational pilots that bridge theory and practice through using open source software as a laboratory while working with mentors from industry partners. Stephen Wally, an executive at Microsoft has been leading this effort. This past summer, he taught with Michael Hilton of Computer Science. We are in active plans to teach this again next summer at CMU's campus in Doha, Qatar and to build a workshop-style version of this at CMU's campus in Kigali in Rwanda providing some global exposure. The final program I'll talk to you about is the Open Forum for AI. And at the key part of this is what we're calling openness in AI. The idea is to build a technical framework that is rigorous and at fine-grained granular level that accounts for all the artifacts of AI and the workflows and the orchestration that connect them. The goal is to build a human-centered participatory inclusive AI that bends the arc towards augmented intelligence and allows a broader ecosystem of players to build smaller, lightweight, more accessible AI models with direct feedback from underrepresented groups. It builds on CMU's expertise in AI particularly responsible AI. I'm pleased to note that the co-director of the Responsible AI Initiative at CMU Hoda Hadari has agreed to lead the effort working with the CMU Ospo. Initial partners include the Open Source Initiative which manages open source licenses and will provide legal expertise and the Atlantic Council which is a DC-based think tank affecting federal policy development. The funding support for all of these initiatives listed here were always grateful for external support but I want to highlight the support that CMU is putting in to both the Ospo and the engine including through library reserve funds. I will end with my standard slide which has a question Jen Stringer stole my thunder but I'll take that as a point of affirmation. It's a provocative question for you but I'm interested in your provocative questions for me. Thank you. Hello, everybody. I'm Dylan Riediger from Ithka SNR and I'm here today to share a couple of data points from a report that we'll be publishing early in 2024 sharing findings from a national survey of research data support services that we conducted over 2023. I should mention my colleague Ruby McDougal who is not here today but who developed the methodology and conducted much of the research data collection and analysis. We are doing this work in partnership with 29 universities who are participating in our cohort project on coordinating research data support services across campus. And the inventory's purpose is to provide a detailed map about the availability and the distribution of research data services, excuse me, data services and it's intended to support decision making about how best to coordinate those offerings going forward and to align them with researchers' needs. The inventory's based on a sample of 40 R1 universities, 40 R2 universities, 40 small liberal arts colleges and a sample of the Canadian Association of Research Library members. And our data collection involved hand searching websites of universities that were included in the sample to identify formal research data services that were being offered by any unit on campus. As you can see from the slide, we collected information on where the service was being offered, what subjects and topics were being covered and some information about the delivery format of those services. I'll say before I move on to share a couple of data points that we defined research data services for the purposes of this project as concrete programmatic offerings that were designed primarily to support researchers in their capacity as researchers. So I'll share two things. First, what services we found and then I'll talk a little bit about which units were offering those services. The first thing I'll point out is that there are quite a few research data support services being offered by campuses these days. We found that on average there were over 12 at a typical R1 and five at R2s. And I should note that this average can be quite misleading because even within Carnegie classifications, the distribution of number of services varies quite widely. Six R1s accounted for something close to a third of the total number of services we found. So there's quite a bit of internal stratification in how active universities are in this space. You'll also notice that general research data management services are unsurprisingly the most common type of research data services that are being offered. This reflects investments that institutions have been making to support open science and data sharing and also shifting regulations by the federal government and other funders related to data management and sharing. I also just wanted to point out in light of an earlier panel today that approximately 72% of the services we found in both R1s and R2s are consulting-based services. The number of those compared to the number of training-based services is very favorable towards consulting and advising services. This, again, I think reflects the value of those services, but it also poses some of the problems around scaling that we talked about earlier today. Libraries are still the primary provider of research data services, but as you can see from these slides at both R1s and R2s, other offices on campus are providing significant numbers of services. The research office and its kind of cluster of research cores and institutes are perhaps the second most likely people to be providing services, and in the full report, we'll talk a little bit about how the profile of services that are being offered differs across which unit on campus is providing them. And finally, I'll just say that, as I mentioned earlier, we'll publish findings from this in early 2024. A few things that will be in the full report that I didn't get time to talk about today have to do with what the landscape looks like in Canada and also at baccalaureate institutions, more detailed breakdowns of the subjects and formats of research data services, particularly any characteristics that are related to what unit is offering them, and also information and data about the distribution of research computing services and data repositories on campus. Thank you very much. I agree it was that good but it was worth breaking the no clapping roll. All right, so I'm Trevor Owens. I'm here to talk about the STACS platform, a system for onsite access to rights restricted digital content at the Library of Congress, where I work. And so STACS is an access system for rights restricted content. It's content the Library of Congress has custody or ownership of, but is not a rental or subscription set of material. It's content that we can make available to STACS terminals placed within our reading rooms and to LC staff, including staff in the Congressional Research Service. Users can search, view and print portions of works but they cannot download or export content. It's a Python Flask elastic search application. You can see a picture of it here on the side where you can also see a rundown of 170,000 books, nearly half a million newspaper issues. It's an exciting, growing part of how we provide access to our collections. So some context on why we built STACS. First off, access to rights restricted content is a key part of our digital collection strategy. I don't expect you to be able to read all this here, but if you do wanna check it out later, it's up online as our digital collection strategy. A big part of this is that as we work towards an E-preferred approach to building our collections going forward, a vast majority of the materials we acquire are not things that we can make openly available online. Expanding open access as much as possible is a big part of our strategy, but we're also focused on a coordinated program of policy and infrastructure to enable the broadest possible access to rights restricted content. So with that, some why of STACS. So as I mentioned, significant portions of our collection materials, whether it's things through copyright deposit, purchase, gift, transfer, or exchange can only be made available on site at the Library of Congress. Another area is that as we shift preservation reformatting away from microfilming to digitization, we're getting more and more digitized material that we can only make excessive, that comes with rights restrictions. Here you can see a wall of book covers. An exciting part of the material we get in STACS is that a lot of this is things from, say the cataloging and publication program where we're getting a lot of widely distributed books that are published in the U.S.  So there are public access terminals for the STACS system in almost all of our reading rooms. There's a full list of them here. And if you wanna see what that experience looks like, you can see Marcus who's one of the digital collection specialists that is really heavily involved with working with STACS, here using one of those terminals in part of our main reading room. But you can see they're widely available across the space. The way that the access restrictions works is that anyone can go to any of those terminals and use them. If you're curious about how that works in practice, so all the material that's accessible in STACS is discoverable through our catalog.  It says on-site access. You go into the actual item availability on the record. You'll see a link that says available on-site via STACS.  And if you're off-site, you go to a page that directs you to the way to get to one of the reading rooms. So here's a little bit of an overview of what's in STACS. So we're now over half a million newspapers and legal Gazette issues. This is largely made up of copyright deposits. So registrations for e-print newspapers come in and are made available in STACS. That's what the Wichita Eagle is here. But then along with that, we also have a huge amount of materials that we're now digitizing that are newspapers from around the world. They go into STACS for access, 170,000 e-books, 700,000 journal articles. But then from there, it goes down to a lot of other formats, music scores, moving images, mapsets, et cetera. And so that is a quick tour of STACS. And I think I can pass this over to our next speaker. I'll walk slowly. My time clock won't start counting until I get there, right? So you can write down the URL. Okay, thank you, everyone. I would like to talk about LUX very briefly with you all. So what is LUX? We think of LUX as a groundbreaking discovery, teaching and learning and research platform, thereby, of course, fulfilling the mission of the university that provides unified, focus on that unified, digital access to the collections of our museums, libraries, and archives at Yale. We launched on June 1st this year with a total of 42 million records and comprising 17 million objects, 13 million works, and millions of places, concepts, people, organizations, and events. And if that wasn't enough buzzwords about why you should care, here are some more. So we think that LUX has an intuitive and highly functional interface. It's built on linked, open, usable data, again, focus on usable, because that enables maintenance and adoption beyond just Yale. It is reconciled and enriched from more than 20 different external data sets, giving access to a vast amount of information that we do not manage at Yale. That happens automatically, and of course, at scale, with 42 million records, we cannot, even with students, do that by hand. And it has all of the functionality that you would expect from a discovery interface, including text, search, and facets, but also leveraging that linked, open data for graph queries. So what were some of the things that came up, which we did not expect? So from beyond LUX, but within the cultural heritage organizations, we found that participating in building data for LUX enabled both the University Art Gallery to contract with an external vendor to rebuild their site based on the data that they provide to LUX at a fraction of the cost of building their previous site. For that, they use external infrastructure, but the Peabody is taking that one step further by using the LUX infrastructure to build their collection site on top of the data that they provide. Going even beyond cultural heritage, it has incentivized people to think about where we can use knowledge graphs at Yale beyond cultural heritage. Can LUX discover relevant research datasets? We have a project which is sponsored by the University Librarian, the Vice Provost for Research in the CIO, to investigate that question over the coming months. Are there benefits to describing core university functions, such as where do you get chicken nuggets from for folks who were in the last session? And of course, not to do with chicken nuggets, though reception will see if there are any. We want to use the data to build AI services that don't suffer from hallucinations by training on some of that core curated knowledge. And if anyone has been playing a drinking game on the number of times folks have said AI, congratulations, you are still vertical. It is after five o'clock. And finally, beyond Yale, perhaps to your wall. So since launching LUX in June, we have had interest from more than 50 organizations who are around the world, including many of the folks here in the room, as to how we did it and can they learn from it? Can they adopt it? So we are currently cleaning the code in order to make that possible. We'll publish it on GitHub in the next couple of months. And we proposed a 12-month project to the IMLS for next year. We'll see if we get it. If we are successful in getting that funding, we will host a meeting here in Washington, DC in exactly 12 months from now, following from CNIFE forum 24, to discuss two primary things of interest. How do we govern this in terms of the data model and the code, the processes and the knowledge? And what are the collaborative solutions that we as a community can come up with to move beyond those first initial technical barriers to entry that can then allow you to demonstrate value internally to your stakeholders in order to then take it through the entire course of the work to come to a product? So if that sounds interesting, perhaps not the chicken nuggets, then please do get in touch with me. My email is on all those slides. Or find me at the reception in a few minutes and we can discuss further. Thank you so much. So hello everyone. I'm obviously the European guy tonight. My name is Juner Roche. I'm from France. I'm the university librarian at the University of Lille and I'm there tonight because I'm also the president of Liber which is the European League of Research Libraries and the voice of research libraries in Europe. Actually Liber is the largest association of higher education and research libraries in Europe and I will try tonight in the five minutes to give you a flavor of what Liber is delivering. This slide is summarizing the main activities we will have. I will get back to the ones in Italic later on and comment briefly the other ones.  We produce a quarterly publication for our members. So 700 people received every other month our internal publication and we also have what we call the Liber Insider which is for everyone. So you can register for free and get access to the main information we provide. We have thousands of subscribers for that publication. We also provide resources and guidance. We publish them on our website. We publish them on YouTube and we publish them on Xenodos so you can get access to hundreds of publications for free videos and so on. We are also working on learning and teaching and provide a lot of content you can reuse again, accessible on our website. We have a publishing activity because we have the Liber quarterly with a high level publication where we publish practical cases and also research articles. And we have a lot of advocacy activity towards the European Commission for instance. What I would like to also highlight is the main events we have. So we have a lot of activity during the year. We have an annual conference, a large one every year. It's open to everyone. So you can register and come and join us next year. We have webinars in common just like the one we had in October with UNESCO and La Referencia. We have what we call a winter event which is more or less an internal event just like you can have in ARL or with CNI. We have architecture seminars and the next one will be in spring in Belgium. And we have, we develop master classes. This is an example on citizen science but we will develop over in the future. And of course things like journeys and other things. We have leadership programs, especially two programs. The emerging leaders which is for second tier librarians. So people who are not university librarians but would like to become in the future. And we have library directors, library journeys for experienced librarians through the world. So it's open to people not only in Europe but also in the rest of the world. We also have a large part of activity about international projects. We have something like 10 permanent staff and half of the staff are working for international projects. For this project are many European projects because you probably know that Europe is spending a lot of money trying to develop high education and research programs. And a lot of them are for libraries tackling with issues like open science, innovative scholarly communication, digital skills, research infrastructures and services and citizen science. So we have there the names of the different programs like shock recreating Europe, Palomera, Diamas project. You maybe know some of them. You have all the details on the website and of course I would be happy to have further discussion about that with you. Nearly the end of my presentation, what I also wanted to stress is the Libre vision for research libraries in 2027. So we have fundamental preconditions in the gray area in the bottom of the picture which are namely upholding rights and values and upskilling the library workforce. And we have let's say three main areas, three main directions which is to have engaged interested hubs to have state of arts services in libraries and to work on advancing or open science. So I will attend the reception. Please come to me and let's start the conversation and don't miss the Libre breakfast round table tomorrow at 7.30. Thank you very much for listening to me. Hi everyone. Thanks for hanging around just a few more minutes. I know we can hear the clinking of utensils next door and the smells wafting in. So I appreciate your patience. I'm Diane Goldenberg Hart with CNI and the first thing I'd like to do is thank our lightning round speakers again. Thank you for those wonderful talks. So I just wanna say a couple of words about breakfast tomorrow morning. We're going to have a few tables reserved for discussion on defined topics. You heard from some of our facilitators just now in the lightning rounds. And there should be up on the screen a complete list of all of the discussion tables that we are setting up for tomorrow. I wanna note that the list that's in the printed program is not the most up to date list. We have some updates. You see them here and they also are on the online schedule, the SCED. In particular, I want to note that we have added a couple of topics, two general theme topics. One is on evolving licensing models and strategies and Lisa Hinchliffe will be facilitating that table. And we've also added a table on AI and policy that will not have a facilitator. This is the first time we're trying that model and with that and all the other tables we welcome your feedback. So just in a minute now, we've asked a few of our facilitators to do a very quick roundup just about a minute each on some of the topics that are on specific programs and initiatives that we thought could benefit from a little more information and context. And the last update I wanted to give about the program is that we will not have a table on information infrastructure and grand challenges from Don Waters, but Don is here and I'm sure he would be happy to chat with you about his project. So without further ado, I'm going to hand it over to Dylan. Hi everybody. Karim Bukita and I have been tasked to take the AI table tomorrow morning. So we will be hosting a conversation about AI and higher education contexts. It'll be wide ranging. Bring what you want to talk about. We're focused primarily on research and teaching applications inside higher ed. We'd love to learn more about what your institution is doing and thinking about this topic and we'll have some things to share as well. Thanks. Good afternoon everyone. I'm Salwa Ismail from UC Berkeley. My colleague Todd Grappone and I will be hosting a breakfast table, 7.30. Talking about how libraries can improvise campus-wide research data services. So for those of us who are not doing super fabulous, super innovative, super cutting edge, how can we move these services forward that then can organically become more coordinated across campuses. So at UC Berkeley, we've reenergized our library data services program in partnership with our campus IT, research IT, College of Data Science and much of it was inspired by our Ithaca SNR big data study and at UCLA, they actually did a campus quite comprehensive study through a survey of their faculty members and then developed a roadmap on this. So join us tomorrow to talk through the approaches we've taken that are taking us forward as we talk about comprehensive, iterative and improvised research data services. Thank you. Everyone, I am Susan Ivy. I'm from North Carolina State University but I'm here today representing EDUCAUS Research, Computing and Data Community Group, also known as RCDCG. So what is the EDUCAUS RCDCG? We're a group of professionals. Many of you in this room are probably a part of it already. If you're not, I hope you'll consider joining and learning more tomorrow at breakfast. We're a group of professionals mostly at higher ed institutions that support researchers as they conduct computationally and data intensive research and all of the things that go along with that as we support that work. So what do we do? We have open calls throughout the year virtually and we also participate in the in-person EDUCAUS conference. We have meetups and we're also considering other ways to leverage resources that EDUCAUS makes available to us for things like working groups in the coming years. So if you're interested to learn more, please come see me tomorrow morning at 7.30. Hi, everybody. I am Alicia Salas. I am a co-lead of the Helios working group on shared scholarly infrastructure and also university librarian at the University of Oregon. And I wish I could go to all of these breakfast tables tomorrow morning, but I'm hosting one. So I wanna invite you to join me. Helios stands for Higher Education Leadership Initiative for Open Scholarship and it's an initiative of over 95 research institutions across the United States at this point. And it is a presidentially driven initiative. So driven by a commitment of university presidents to work towards aligning incentives for open scholarship within higher education and across sectors, including government and industry and higher education. So I would like to invite you to join me at my table in the morning for breakfast to chat more about that work, ask questions, learn how to join or share ideas and current Helios members and non-members are equally welcome. Hope to see you tomorrow. Great, thank you to all of you, to all of our facilitators and to all of you for attending. And with that, we are adjourned for the day. We'll see you next door at the reception. Thanks so much.


Environmental challenges, especially ocean acidification and pollution. Greenhouse gas emissions are increasing the acidity of our oceans and corroding fragile marine life. To better understand and measure these impacts, scientists are using the calcium-45 radioactive tracer technique. They set up experimental aquaria with seawater at different pH levels and add calcium-45 as a tracer to measure the difference in the growth of corals and shellfish. Gathering this data is vital to protecting the health of coral reefs which are so critical to marine ecosystems and coastal communities. Today people are facing dangerous levels of contaminants in their seafood. From heavy metals, pesticides, microplastics and more. Using techniques based on mass spectrometry, scientists are able to identify the level and source of pollutants in seafood. When analyzing a sample, they remove all its organic matter, leaving only the contaminants behind and then use specialized analytical instruments to determine contaminants present in the sample. Having this data helps coastal countries make appropriate management decisions to regulate pollution. These nuclear and nuclear-derived techniques provide a unique window into understanding the processes behind ocean change. An understanding that can help us secure a healthy ocean for the future. The IAEA, through its marine environmental laboratories and its technical cooperation program, supports member states in acquiring and efficiently using these nuclear techniques. Gathering this data is vital to protecting the health of coral reefs which are so critical to marine ecosystems and coastal communities. Today people are facing dangerous levels of contaminants in their seafood. From heavy metals, pesticides, microplastics and more. Using techniques based on mass spectrometry, scientists are able to identify the level and source of pollutants in seafood. When analyzing a sample, they remove all its organic matter, leaving only the contaminants behind and then use specialized analytical instruments to determine contaminants present in the sample. Having this data helps coastal countries make appropriate management decisions to regulate pollution. These nuclear and nuclear-derived techniques provide a unique window into understanding the processes behind ocean change. An understanding that can help us secure a healthy ocean for the future. The IAEA, through its marine environmental laboratories and its technical cooperation program, supports member states in acquiring and efficiently using these nuclear techniques. Okay, should we start? So let's start with the part two of our event here at the COP26. I would like to welcome all of you again and for those also who have just joined us for this part of the event. We are at the COP26 talking about climate change, climate change adaptation, climate science and ocean change. And in particular the role that nuclear techniques and nuclear technologies can play in this context. We just concluded the part one where we have heard a lot about how climate science and nuclear science is connected.  Now in this part of our event, in the second part, we will look into the connection between ocean change and climate change. And how nuclear applications, nuclear techniques, nuclear science can make a difference. The IAEA, and many, many across the world don't know this, has its own environmental laboratories, which are in Monaco. And these labs, the scientists, they gain knowledge and insights on how the ocean is changing. We are talking here about changes that are related to the marine pollution, to habitat destruction, to biodiversity loss, to plastic pollution, to ocean acidification and so on and so forth. So some of those scientists who work in the environmental laboratories in Monaco are now here with me in this event and have joined me virtually. And they will highlight the role that the agency is playing in this important scientific effort. And they will better explain now the issues that we are facing when it comes to ocean change and climate change. With that, let me turn to my first expert, Dr. Mark Metian. He is a researcher at the moment at the environmental laboratories of the International Atomic Energy Agency in Monaco. So Mark, I would like to welcome you to this event at COP26. Thank you for being available to talk to us about ocean change and in particular ocean acidification. Can I ask you, Mark, could you briefly tell us about what actually is that connection between climate change and the ocean or the oceanic system? Of course, Martin, thanks for inviting me. So yeah, it's really simple. Oceans is playing a huge role on climate in general on Earth. As you may know, it covers 70% of the surface. Ocean regulates global climate in general, may get temperature, drive the weather. So it's something that we cannot deny. And now with the increase of carbon emission, like burning fossil fuels and so on, and the climate change that we're talking about, the ocean are changing. So this is what make the big connection. So the huge role of the ocean in general and now the connection with the carbon, the reaction with climate change. Basically, you are saying the oceans are changing. Now, in what way are they changing and what is the impact of that, what we call the ocean change on all of us? Basically, I mean, the list is very long. But among the ocean changes, because they are plural, expected already observed, I would say that more extreme weather events changing ocean currents, rising sea level rise, rising temperature, melting a sea ice and sea ice. But also the chemistry by itself is changing. So we can see, we can attend now already the increase of apoptic to apoptic weather. So when the oxygen into the worries is reducing and ocean acidification, as you mentioned. And this is key because the problem is livelihoods and humans in general are really depending on oceans for numerous key goods and service. And they are generally under threats with this climate change and thus reaction to ocean changes. Right. With that, Mark, you have already anticipated my next question. And that is, why should we care about ocean acidification? It sounds like something that scientists should care about. But what impact does it have or could it have if it accelerates with climate change? What impact could it have on all of us? Why should we care about this? As I said, basically for the specific case of ocean acidification where the water become more acidic and there is a different reaction occurring in the water, basically the risk is that some of the organism will have some troubles. So the biodiversity could be impact. And among those, I mean, we know that since the ocean acidification will deeply impact calcifying organisms. The organism that is building this shell or the corals, when you know the corals, there is this skeleton. And basically the ocean change and the ocean acidification will directly impact those organisms. And this is why it's so important because a lot of people in the world and the global population are depending on this for the goods and service. For example, seafood, for example, as a food, for example, as tourism for when we talk about corals affected by ocean acidification, then you have this coral reef that will be impacted. So protection of the coast and so on and tourism for divers and so on. So it's a lot of impact that we expected. Already in US, a few years ago, there was a big impact on agriculture production of oyster, for example. So we saw it and it will be worse in the past. So that's why it's so important. Right. So Marc, you're working in Monaco in the laboratories of the IEA. So what kind of research and work do you do in this space? And how is that relevant, the work that you do there in the context of this discussion of ocean change and climate change? Basically, the IEA is working hard on to assess the impact of oyster acidification using nuclear techniques. So basically, we are using different tracers. I'm not going to details, but tracer, for example, calcium, because as I said, the calcifying organism will be impacted. So we want to know at which rate, at which, what will be the quantified this impact on different organisms that are commercially important or that are important in terms of biodiversity. So we play with our tools, the radio tracer, to try to assess what's the impact. The same way also we'll try with other techniques to try to understand how the organism, the health or the organism are impacted too. So we'll use the way they eat, the way they swim. All these impacted will use other tools, the audio radio tracers. And in addition, of course, we are pushing for the use of this technique for other aspects. For example, we know now with some use of isotopes, we are able to track and to record the past history of ocean acidification. So we know now that we are really in a condition that is different than the past and not so good for the organism right now. And of course, when we talk about the action of the IEA, we try to help as much as possible the member state to report, for example, the ocean acidification in the ocean and also transfer our knowledge and the know-how about these techniques to assess the impact. And now, just to finalize on this, it's also important to know that in front of the science that we'll push is basically also to try to find a solution. So in the sphere of scientists right now, they are mentioning some alkalinization process and that's what we'll go for. Try to see if the different hypotheses or different solutions that are offered by scientists are viable. And so in our laboratory, we are trying to test that with our techniques, of course. Right, Marc, my last question to you. The knowledge that you are generating in the laboratories that you have been describing on ocean acidification, on the connection between ocean change and climate change, how does that knowledge actually get out of the lab? You know, we don't want that knowledge to stay within the lab. We want that knowledge to get out of the lab. We want countries, member states, developing countries to benefit from that knowledge so that they can use it in their own adaptation strategies. How is that being done? Basically, it is the different ways, but the most important one is when we work in hands of our colleagues from technical cooperation projects. So nationally, regionally, or between region, we are providing expertise. We are helping to build some capacities to train people with experts and so on. So we are pushing for that. So it's not only our aspect or in our lab also. We welcome scientists to learn how to do it. And so this is really important, as you know, to make this science out of our laboratory. So we develop the technology and push the technology to the forefront, and then we transfer as much as possible. And sometimes also we collaborate with some member states that are already in advanced level for this science to benefit other member states. So this is really important for us in Monaco to really push for this transfer. And the techniques are the fact that the techniques are not only in our labs, but also in the labs of the member states. Excellent. Thank you so much, Marc, for being with us here on this event here about ocean change, climate change at the COP26. Now, let me turn to our second speaker. And I would like to welcome Ms. Ashley Bantelman, who is a project officer at the IA's Ocean Acidification International Coordination Center. So Ashley, welcome to the event. Thank you very much for being with us, for being available for this talk. Now, I would like to ask you the following question. We have heard just now from Mr. Metian about what ocean acidification is and how the IA is studying its impact. IA has something that is called the Ocean Acidification International Coordination Center. Could you please tell us more about the center, what is the role in this whole picture in this context of our discussion, which is, and I repeat, the connection between ocean change and climate change. Yes, thanks Martin. Hi everyone, welcome. Thank you for being here with us. So, as you've heard from Marc, ocean acidification is a big issue. And when IA member states and the rest of the international community started to realize that this was an issue that IA responded by launching the Ocean Acidification International Coordination Center. Back in 2012, so it's been around for almost a decade now. And the agency launched it in an effort to promote international collaboration on this topic of ocean acidification. So through the center, the OAICC as it's known, the IAEA is helping IAEA member states better understand the impacts of ocean acidification on the diverse marine ecosystems worldwide. And the center is working in three main areas in science, capacity building and communication to spread awareness about these impacts on member states and potential livelihoods in the member states. So since its inception almost a decade ago, the OAICC has worked to build and implement a global project. So we're not just, you know, we're located here in Monaco, but we're working with global partners all around the world on the issue of ocean acidification. And we're also working to promote the IAEA's mandate of applying novel nuclear and nuclear drive techniques to address climate change impacts to the ocean. So over the past decade, we have helped to raise global awareness of the issue of ocean acidification. We've led experimental research efforts. We've engaged member states on every continent to become active in understanding the issue of ocean acidification. And we do this by conducting specialized training activities and other capacity building activities. So now being around for almost a decade, we have become an essential player in the international leadership on ocean acidification. Excellent. Ashley, the center has been around for almost a decade, as you said. And you're doing capacity building, you're doing training, you are engaging with countries and member states and with their scientists on this topic of ocean acidification. Would you say is there anything tangible in terms of results that this work has generated? Is there anything that you have achieved that you are proud of? Is there anything that our member states, our countries or representatives that are listening to us can go to and say, oh, here is a wonderful, you know, outcome or product that is useful for them? Yeah, thank you. So just to give you kind of some numbers, the center, the OICC and our global partners have worked together on more than 850 capacity building opportunities. So it's a huge number and this involves more than 750 scientists from more than 100 IAEA member states. That's in over the past decade. And so just to give you a more kind of detailed example, our capacity building activities fall into four levels. There's the very basic just sharing theory on what ocean acidification is. Then there's another level, level two, which is basic, but it's basic applied training. It's a little bit hands on and then there's level three, which is a bit more advanced and then level four, which is like coordinated research projects. But to just give one example, we had a recent level two training course in Kenya. So it's a basic hands on training. It was a regional training course and it was organized to provide some information and training participants and how to design a basic ocean acidification experiment. Taking into account local needs and local capacities because in the laboratories in Kenya, they didn't have necessarily the most advanced setup like maybe what we have here in Monaco. So this worked really well and best practices were developed from it and it kind of showed that we can do good science even with a basic laboratory setup. And it was like I said, it was very successful. The first data were collected and they're already ready to be published into a scientific publication. So then aside from capacity building, we also offer a number of different resources for scientists and for our member states. So one of those is the bibliographic database. This is an extremely useful tool for scientists and researchers. It's home to all scientific papers written on the topic of ocean acidification and it's updated regularly by our team here in Monaco and it currently has more than 9,000 references. Yeah, thank you. Thank you Ashley. That sounds fantastic. My last question to you. This is an international center and so you must have some international partners or collaborators. Which are the partners or donors or affiliated institutions that you are working with in the center? Yeah, so as you've heard from Mark, ocean acidification is a global phenomenon. So it's not some, you know, from the very start we have worked to build this international network and the success of the project hinges on our strong collaboration with our partners. Some of them are the Intergovernmental Oceanographic Commission, IOC UNESCO. We work with the US National Oceanic and Atmospheric Administration Ocean Acidification Program, the Ocean Foundation, the Ocean Acidification Alliance or the OA Alliance. And then we work with countless regional and country specific partnerships around the globe as well.  Very good. Thank you so much Ashley for being available for this talk. Thank you for the insights that you have provided and for, yeah, making this center on ocean acidification visible in the international context beyond the nuclear community. And that is what our event here is all about. So thank you so much and I will turn now to our next and last speaker. And I'm very delighted to have Professor Peret Masqué here with us. Peret, you are at the moment a research scientist at the radio ecological laboratory of the IEA in Monaco. And we are delighted that you are with us and I would like to ask you a couple of questions. My first question to you is the following. We have heard from the previous two speakers here in this part of our event that the oceans are taking up a large fraction of the atmospheric CO2. And during the last decade, also, we have increasingly heard the term blue carbon in relation to nature-based solutions to climate change. But can you, Peret, please explain to us what is behind this term blue carbon? Yes, hello Martin. Thanks for having me and thanks for organizing this event. Yeah, the blue carbon, well, we call it blue because it's the ocean and we assume the ocean is blue. Blue carbon refers to this capacity of the ocean to accumulate, to sequester carbon from the atmosphere, right? So, Mar talked about that before. The CO2 in the atmosphere is in sort of equilibrium with the ocean. So, eventually, some of this carbon is fixed by the plight of plankton or other species, plants that live in the ocean, through further synthesis. And then it can be sequestered. Now, blue carbon in general would refer to all of this happening in the ocean, including coastal and open ocean. So, in the open ocean, we talk about the biological pump, which is the trituse, the particle sink, and so they explore the carbon. So, we sequester this carbon and it cannot be going back as CO2 to the atmosphere. Now, in the coastal ocean, however, what we find is something very unique, which is what we call coastal vegetated ecosystems, that is mangrove, salt marshes, seagrasses, right? They occupy a tiny amount of area in the ocean, less than 1%, much less than 1%, but they have a great capacity for sequestering carbon. So, this carbon that's accumulated in the plants because of photosynthesis, eventually, it's accumulated in the sediments beneath. So, this is something different to what happens in the forest and land, which the trees do the same, right? But they don't accumulate sediments, so the sediments don't grow. In the oceans, the sediments grow. They accumulate at different rates, depending on where we are, but they bury this carbon and they sequester it efficiently, very efficiently. So, basically, we're talking that about 50% of the carbon sequestered by the ocean in the sediments happens in this little amount of space. So, the carbon that is sequestered then cannot be or is not being released to the atmosphere, and that is obviously very important. But, Peri, how does it work for how long, for example, is the carbon stored in the ocean or sequestered by the ocean? Well, this has two answers. If the system works well, if there is no problems, if there is no changes in the system, naturally or artificially, these sediments shall stay there for thousands of years or beyond. We've actually seen this. I mean, seagrass meadows, for instance, where they accumulate sediments for thousands of years that we've made with carbon-14. However, if we perturb these systems, if we, for instance, destroy a seagrass meadow, we deforest a mangrove forest because we use the wood, then these sediments are subject to being resuspended, for instance. And when they are resuspended, the carbon, organic carbon, which is in these sediments locked because it's in toxic conditions, then it happens to become in contact with the oxygen that may become then CO2 because it's respite by bacteria, for instance, and then this CO2 may be emitted back. So what the problem is that when we destroy these habitats, that can happen naturally as well. But basically what has happened during the last century or so, we've destroyed a large percentage of these habitats worldwide. What happens is that two problems. The first one, we lose this capacity. We're not sequestering carbon anymore, so the ocean is not helping us, let's say. But at the same time, we run the risk and it's happening that we're emitting CO2 back into the atmosphere, first to the ocean, then into the atmosphere. CO2, which was carbon, that had been there for hundreds of thousands of years. So in one go, we are losing two things. Right. Let's talk about coastal ecosystems. What do we know about the capacity of coastal ecosystems? And here I'm talking about, let's say, mangroves to sequester carbon. Is this a new frontier in the research? Is this already established knowledge? And where are we on this question of the coastal ecosystems in terms of carbon sequestration? So this subject started being seriously studied in terms of climate change about a decade ago. That's when they coined this term, blue carbon. What we know is that they are very efficient. What we know, there are differences depending on where we are in the planet. Mangroves in arid areas are not the same as mangrove tropical sites, for instance. There are very different seagrasses species. It depends on the physiology of the sites, etc. But their capacity for sequestering carbon varies widely depending on where you are in the planet. Questions, there are two main aspects here. The first one is that a large area of the planet has not been studied. I'm going to just give an example. Brazil holds about 10% of the mangroves worldwide. And there is a handful of studies about this carbon sequestration capacity in Brazil. That's, of course, very little because we're talking that at the very least, the carbon sequester in Brazil by mangroves would be about 10% of what's going on worldwide. So that's a limitation. Now, what I explained about how the particles sink and accumulate it in the bottom sediments, it's the basics, it's how it works, but it's simple. There are more processes there. This is an aquatic environment. So this carbon, eventually in the sediments, can be respite by bacteria, can be transformed. There are different types of carbon in the system, more or less labile. So we need to focus more efforts on what's going on. And there is then a third aspect. We talk about restoration of all these ecosystems. So that for a number of reasons and not only for the carbon sequestration capacity, because of course these ecosystems are important for a wide number of aspects in terms of what some call ecosystem services. But where we want to restore them, we need to assess what is actually the deal. We just published, for instance, a paper showing how different is the response in terms of carbon sequestration when we restore a salt marsh in a tidal or non-tidal environment. That's it to say whether the salt marsh is going to be always or more or less submerged in water because there's going to be growing plants. There's going to be a number of biochemical processes in there. And you can have at the same time there's sequester carbon, you can have emissions of methane for instance. And methane also is a greenhouse house. That needs to be studied. It's not as simple as it may seem. However, all the indications show that the potential role in mitigating climate change with restoration compared with other natural solutions like forests online are at the very least of the same order of magnitude if not higher, particularly for microbes. In terms of your work with the IEA, the IEA and the labs in Monaco, they're using nuclear techniques or nuclear derived techniques, isotopic techniques. So what is happening in terms of the IEA's involvement in this particular area of work that you are describing? What is the IEA doing? What are the labs in Monaco doing? What are you doing on this? That's a question I've heard a number of times. Why the IEA would care about this? I mean, what's the deal with nuclear techniques? There are several aspects here. But the main ones are two. First of all, carbon has several isotopes and a stable isotope has two 12, carbon 12 and carbon 13. I'm not going to go too technical in here. But depending on how much of each of the isotope we have, we can then understand what's the process of this carbon. It comes from this kind of plant. It comes from the other one. It comes from the terrestrial environment, et cetera. And we need that to understand what is the capacity of these ecosystems to sequester carbon. It's not the same if they sequester the carbon that's produced, that's fixed by the plants in situ or it's actually accumulating the carbon coming from the lab. The other thing is what I referred to before, which is we need to know not only how much carbon we find in the systems, in the sediments, bar it in there for years, hundreds of years or thousands of years, but we also need to know at which rate. In other words, if we say it accumulates this much, a number, a hundred, whatever a hundred is, in one year it's not the same as it accumulates it in a hundred years. So we need to date this system. We take the sediment course and we use techniques which many of whoever is attending this know about carbon 14. We use other techniques and we establish the rate at which carbon is sequestered, at which the carbon is accumulated in the sediment. So we know the actual efficiency of the systems in sequestering carbon. And of course then, this is what we do in Monaco last, but then as Mark explained before, and actually what we do is a lot of technical cooperation and research projects worldwide. So we assist and we collaborate with dozens, literally dozens of countries in doing this in any part of the world. Peri, thank you so much for explaining to us the blue carbon and how the IEA is also involved in this. I'm afraid that we come to the end of the part two of our event. We come to the end of our event. We are running out of time, but I would like to thank you and your colleagues for making yourself available to shed light on this very important issue, which is the connection of climate change and ocean change. Let me with these words then summarize or thank everybody who has been with us here, summarizing in the sense that it is not well known across the world that nuclear technologies, nuclear techniques, nuclear science is relevant for climate adaptation and climate resilience. We believe that now after this event, and we will distribute also all the information that was talked about and transmitted here today, we will distribute it through our various social media channels that this information will help to make this work more visible and to also help our member states, the countries to get access to these kind of technologies so that they can better adapt to the climatic changes that they are going to face. I would like to thank everybody for being here, for staying with us. I know it's a Saturday afternoon going into the evening, so it is not so easy to be motivated and to be here, but I would like to thank everybody and with that we conclude our event. We have for those who are here physically, we have some, I'm afraid it's not beer or wine, I'm afraid it's coffee and tea and cookies and so on, but we have something here behind where you can go and we do some networking and a further exchange of views. Thank you very much and goodbye, have a nice weekend.


Hi, welcome to theCUBE. I'm your host, Lisa Martin, and we are on the ground at Google with Cloud Now, which is a nonprofit organization with leading women in cloud computing and converging technologies. Tonight is Cloud Now's fifth annual top women in cloud innovations award event, and we're very excited to be joined by one of the winners tonight, Diane Marsh, who is the director of engineering tools at Netflix. Diane, welcome to the program. Thank you. It's so exciting to have you here. We'd love to know a little bit about the project for which you are receiving the award tonight. Talk to us about kind of the impetus of that and how you're leading this team to success at Netflix. Great, so as you said, I'm the director for engineering tools at Netflix. So that means that we build the self-service tools that all of the other engineers at Netflix use to take their code basically through build, packaging, and then deployment to the cloud. And so it was really important to us since it's self-service that we provide a really great self-service experience, something that all the engineers can do. They need to deploy their own code. They need to roll it back if there's a problem. So we build a tool, a continuous delivery platform, that allows them to not only deploy their code, but send it through several gates along the way. This is a flexible platform, because if you say continuous delivery to people, people are like, wait a minute, I'm not ready for that train to go, right? I might need to stop in the way. So we built one feature into that called manual stage, which if you can't completely automate your process, you can still participate on the platform, because you can always have that, I need to push this button myself, or I need to acknowledge an email or something like that as a manual stage in the process. Fantastic, talk to us about the team that you're leading on this project. The team is made up of about eight engineers, but the interesting thing about it is that, this is an open-source project, and so not only did we build support for the Amazon endpoints, because we're all in AWS, but we reached out to our friends in the industry, and Google worked on their Google Cloud endpoints, Microsoft is working on Azure endpoints, Pivotal worked on solutions for Cloud Foundry, Target and Veritas worked on OpenStack, and so this is a huge collaborative effort that not only produces a product that everybody can use, but also just brings together a bunch of people who are working on this product and kind of the mind share in the industry to share ideas and to sort of collaboratively build this project. I was gonna say exactly, it sounds like a really great facilitator for collaboration. From a background perspective, you're in engineering, did you grow up as a kid being fascinated by engineering? How did you get to being in this senior management position at Netflix? It's a really interesting question. I grew up in the, I was in high school in the early 80s, and we didn't really have computers in high school then, we didn't have computers at home. And so I didn't have any exposure really to what computer science would be. And at the time, we all went through these aptitude tests, right? Where we took tests and they told us, the test told us what they thought we would be good at. And it turned out that I was probably not a big surprise to me really good in math and science and also in languages. And so it was kind of an interesting confluence of, well, math and science and languages, maybe that would be computer science. And at the time my mom said, I think that computer science is gonna be a really big thing. What was really interesting is, Julie Hannah in the keynote today said, her mom told her exactly the same thing. So I think the message here is we all need to listen to our mothers, right? Right, right. But yeah, so that influenced me a lot. We didn't have, only the chemistry teacher had a computer in my high school. So I didn't really see a computer till I was in college. So in terms of you had this aptitude for it, your mom was very encouraging, which is really fantastic and not something that we hear a whole lot. From a barrier perspective to women in tech today, I think we're all familiar with a lot of the statistics. One of the things that intrigues me is the social influence. When you were really developing your skills and finding this passion, this aptitude that you naturally had, were you influenced or did you find that confidence or maybe some of the psychological components or an issue for you? Or did you really just feel like, this is what I meant to do and I love it? Yeah, I think I was kind of just stubborn about it. I don't feel like I ever had any real barriers. I went to a male dominated university, so it was about four men to every woman in the university I went to and computer science was even less populated than that. And maybe that early stage of setting expectations of being one of the only women in the room, maybe that's helped me throughout my career because it was never surprising to me to be outnumbered in a meeting. I agree with you, I've had the same experience in the last 11 years I've been in tech and it's just been something I've been used to, but it's interesting, here at theCUBE, we're the flagship show of SiliconANGLE Media. We've been focused on promoting women in tech for several years now, really recognizing there's a need to amplify the voice of these female leaders, everyone's familiar with Cheryl Sandberg and Comstock, some of the other leaders, Meg Whitman. But I think it's an interesting, I love your background that you were not really hampered by that from a competence perspective. I think that's a great message that other young girls need to know. As we look at some of the barriers, there's also tremendous amount of opportunity and I think your recognition as one of the top women in cloud innovation and cloud now is an organization Anita Borg and others are great demonstrations that we are moving the needle forward. But something that you and I were talking about before we off camera was really intriguing and attrition is over 50% right now for women that start tech careers that leave for other careers. And that life cycle is something that would, I think be very interesting to explore, but you had an interesting experience after you had your second child that was quite intriguing. Can you share that with us? Yeah, so after I had my second child, I was at a conference and I was talking to a woman who wrote C++ books and she's really a pillar in the industry, Nancy Nicolaiuson. And we were just sort of talking and she said, you know, I listened to all the topics that were talking about these conference and there are some things that are new. There's some really great concepts and new ways of looking at things but fundamentals are strong in people that have a degree in computer science and you can learn all of these things. And she said, I think that when people take time off after having a kid, they feel like they have to learn everything that happened in their absence. And in an industry that moves super fast like computer science, that might be a little bit overwhelming. And she said, I don't really think you need to do that. I think use the speed that this industry moves to your advantage, jump to the forefront of technology and start working there, start learning there and recognize that most of your male counterparts who haven't taken time off to have a kid haven't been staying up with technology either. And so, you know, keep that as confidence in yourself. I think that was super important. I think as, you know, we tend to lose women around child, you know, having when they have their children and if we can give them confidence to come back that this can be a welcoming field and that they don't have to be intimidated about what they missed while they were gone. I think that's super important. That's fantastic advice. Use that speed to your advantage of technology. Thank you again for being on theCUBE. Congratulations on being one of the top women in cloud innovation at the Cloud Now event. Thank you so much. It's been great to be here. And if you know a woman that should be featured in our segment, in our Palo Alto studios, tweet us at theCUBE, hashtag women in tech. I'm Lisa Martin for theCUBE. Thanks so much for joining and we'll see you next time.


Hi, my name is Narayan Mandelika and I'm a Group Product Manager at Work. Today, we'll be discussing about the build versus buy analysis. A little bit of introduction about myself and some background about myself. I'm a Group Product Manager currently in the platform engineering team at Work. The platform engineering team is a centralized team that provides the tools and the products to be able to develop software at Uber and be able to deploy that software and manage it in production. We essentially provide the infrastructure and the platform to be able to run all our services on top of. Prior to Uber, I was a Product Manager at VMware managing the Kubernetes Management Platform. And before that, I was at HP managing the Cloud Management Platform. Prior to being a Product Manager, I was an engineer working in the payment gateway, infrastructure management software, and the cloud automation software. So now let's talk about the build versus buy analysis framework. It's essentially a decision making framework for software companies who have both the engineering prowess as well as the IT budget to be able to buy software. And it is these companies primarily who actually go through this kind of an analysis so that they are able to figure out how best can they use their engineering resources internally to solve their business problems. And whether a given business problem needs to be built in-house or should it be actually something that they need to look out and get a product and integrate that into their environment. And the outcome of such an exercise really varies, you know, company to company, you know, there are several both internal and external factors that could influence the outcome. Many things such as, you know, the culture of the company, the maturity of the company, you know, the confidence in the engineering talent would actually play a big role in the end outcome of such an analysis. Now let's talk about why this is important to organizations. One, it promotes a healthy debate before even trying to solve the problem. You have to consider various factors such as time to value, how critical this problem is to the organization, how it aligns with the goals of the overall organization. Before you attempt to kind of go about either building or buying software and all these, you know, decisions have to be made upfront. It's also a great opportunity for PMs and engineering teams to be able to look at various products and understand the, you know, the problems and the features that they're trying to solve. And also, you know, during this process, both the PMs and the engineering teams would probably uncover some interesting use cases, especially because, you know, the vendors would be interacting with many of the customers and they would be enriching the products based on their conversations. And some of these use cases would probably be applicable to the, it's our PR, our desktop. So that's something to consider. The other is that it could also result in some good partnerships, since you get to talk to various vendors, leaders in the same problem space, as you go through the evaluation process, and now you can also continue to build that working relationship and collaborate and partner with them, as you implement some of the solutions within your organization. It could also lead to some interesting options down the line. For example, if you're trying to solve a mission critical problem or a problem that is going to help you in differentiating significantly from your competition, your organization may be willing to invest in maybe acquiring maybe an each player or a small vendor, so that you're able to kind of acquire for building the talent, and maybe you may be able to quickly build this capability out or extend the vendors product that you have acquired, so that you're able to kind of bring in that time to value. At the same time, you're able to cater to specific internal needs that your organization might have. So now let's talk about the phases of analysis. You can think about this entire build versus buy analysis to be primarily of five main phases, at least that's how I perceive this to be. One is the build cost estimation, which is an internal estimation that you have to go through to be able to understand what does it take for your engineering team to be able to build this product out, operate on this product, and then also, you know, what does it take for your team to be able to maintain this product and the infrastructure product, infrastructure, the cost of running this product in the house. During this process, you go through an elaborate process of identifying the requirements and validating which funds are critical for your organizations, which funds could wait, et cetera, and we'll go through this, the factors that we use as part of the build cost estimation. The other is the vendor evaluation process. You identify the vendors of solving this problem, and you evaluate these vendors. Again, we'll review the factors there. The product evaluation phase is identifying the products, how best they meet your requirements, and then finally, shortlisting those products to be able to go through a POC phase, where you get much closer in terms of the details, right? I mean, you test the product, you experience the product so that you're able to evaluate this product in your environment or integrating it into part of your environment, and then, at the end, you use all the data that you've collected and frame an opinion and be able to do a build versus buy or even partner analysis. At the end of the day, you actually have to make a decision whether you want to build by it or you want to partner with one of the vendors of that. I've indicated in some of these phases where you would be better off by partnering closely with engineering so that the outcomes are going to be a lot more meaningful for your organization, and you definitely need the engineering partnership, especially when it comes to estimations, when it comes to actually integrations during POC, et cetera. So now let's also consider some of the factors before you go into doing the analysis. The factors that you have to consider is the level of maturity of the problem area. You can probably use the Gartner curve, Hype curve to be able to determine if this problem is something that many vendors have already addressed. There are mature vendors in the marketplace with mature products addressing this problem area, then why go reinventably? So you probably want to kind of lean towards more of a by decision unless there are factors, outliers that are very specific to your organization. The other is you have to look at the organization strategy and the goals. How critical or mission critical is this problem that needs to be solved? And then the pulse from the leadership is another aspect. You need to support from the leadership before you go through this analysis because it is going to take a substantial amount of time, both from the product management and the engineering team, as well as down the line the leadership. And the net outcome from this exercise need to be supported also by the leadership so that you are able to get to the implementation phase after the analysis is complete. The culture of the organization, again, look for whether there's a bias within the organization to be able to build or buy. And then the other aspects to look at is what is your confidence within your organization, whether your engineering team can actually pull off this by building something or would you encounter hiring challenges? Let's say if you don't have enough engineering resources to build this capability out, then you probably want to kind of lean towards more of a buy if you notice some of those challenges. Also look for what is the current and future IT budget based on your current and future projected scale? This is going to be very important both for the build as well as the buy. The current and future IT budgets will be required from an infrastructure point of view if you were to build, but you need to still host this product in your environment and maintain and manage that. So you have to think about the infrastructure cost of doing that. If you are to buy, then you are looking at the licensing cost and potentially other costs such as network, egress cost, etc., from your data center or from your cloud provider. So these are some of the things that you have to think about before going into doing the analysis. So now let's talk about the first phase of doing the analysis. The first phase is called the build cost estimation. The build cost estimation phase, this is the first phase where you first prioritize all the features so that you are able to identify the most important ones you need. This list is going to help you in assessing the vendor products as well down the line. The important features, as we call them as priority zero features, are going to be what defines your minimum viable product. Make sure that you work with engineering in the space. Identify what takes engineering to deliver these features in the shortest timeframe. Essentially, you look at how many people can work in this product in parallel. This is going to be your internal time to value if you were to kind of build this product internally. This factor is something that you use to be able to assess your vendors as well because the vendors may not have all the features that you ask for. So you have to look at, you know, what is the value of the product? You have to be able to assess your vendors as well because the vendors may not have all the features that you ask for. So you have to look at, you know, what is the meaningful timeframe by when you get these minimum capabilities that you're looking for from the vendors as well when you make this assessment. In addition to this factor, the time to value, you also have to assess the engineering man-mans to build these capabilities out. The engineering man-mans will give you a rough estimation of the cost of building the product, taking, you know, the average cost of engineering in your company and also where you desire to build this product. You can actually determine what's the average cost per engineer to be able to determine the overall cost of being able to build a minimum viable product. Add to this the infrastructure cost, the integration cost, the cost to be able to maintain this product. So this is going to give you like an estimate of what the build cost estimation is going to look like. Now let's look at the next phase of the evaluation, which is the vendor evaluation phase. In this phase you will identify the vendors who solve this problem with products. One way to look at this is you can go through the magic quadrant from Gartner, identify the leaders in this phase. Evaluate these vendors for financial stability, whether they fit the product, whether they consider the product to be a key part of their overall strategy. And if the product is clearly in alignment with the vision and the mission that they state. Also evaluate if they are a strategic vendor to your organization. This helps you in ensuring that you have leverage with the vendor. Also assess how responsive the vendor is. During the entire process of engagement with the vendor, make notes of how responsive the vendor is so that down the line if you were to go with this vendor, you want to make sure that the vendor is able to cater to your needs, address issues on a timely manner. And as a result, you would see better success if you were to go with this vendor, if you deploy this particular product in your organization. Look for vendor reference feedback. Ask the vendor to share references. This is going to be crucial for you so that you know that you are not an outlier, especially when it comes to scale, when it comes to certain use cases. You have other customers who are using the product. Similarly, as a result, you'll have better success in getting the vendor to prioritize and address some of the missing adjacent capabilities in the product down the line. So these are some of the things you consider as part of your vendor evaluation process. Now let's look at the product evaluation phase. Once you have identified the vendors and their products have been shortlisted, you can use the following factors to determine which of these products best suit your needs. At this phase, you get to evaluate these products through vendor demos, maybe testing the product out using test accounts. And once you do that preliminary evaluation, you know, you should be in a position to be able to at least identify two or three products that address this particular problem area. Obviously, you first start with, you know, in terms of the factors, you first start with looking into the features and capabilities that the product support, making sure that these products offer the minimum viable features that you are looking for at least to begin with. Check for scale, check for availability and throughput requirements ensure that those requirements have been met, you know, and also as you go through the POC phase, we talk about some of the other things that you could do to be able to evaluate the product in terms of scale availability and throughput. The licensing model is going to be a key aspect to look for. A good licensing model helps you in being able to predict the licensing cost based on your current and future projected growths. Right, the licensing model also needs to be modular so that you can pay for what you use from the product. And down the line, we will also talk about the options and you may decide to just use part of the product, not the whole. And as a result, the licensing model should ensure that you're only paying for what you're going to use. Use the licensing model to be able to estimate the licensing cost based on your current scale as well as future. Also assess the product for, you know, whether there's an open source availability. Because it does help you in de-risking the product, you know, implementation. For example, down the line for some reason, the vendor decides to not support the product anymore. You have the option and the viability to kind of go in and start bringing back the open source product and contributing to it and enriching the product. The other is having an open source model helps the vendor accelerate the features and capabilities because now you have the community also contributing into the open source product. Also, your engineering team can also contribute to certain key features that are very important to your organization. And as a result, the vendor can also accelerate that by pulling in that open source version into the commercial version. And as a result, you get the benefit from these features showing up much sooner in your environment. Look into also the feasibility to integrate into your current ecosystem. This is going to be really important because, you know, how does this product integrate into your user identity management? How does this product integrate into your monitoring subsystem, logging subsystem? These are all going to be very important to be able to manage the product and you don't end up with having, again, like a sprawl of other products that you have to bring in just to integrate this product with. So now let's talk about one of the most important phases, which is the POC phase, which is also one of the most lindiest phases, I would say. You know, once you have shortlisted the products, preference, as I mentioned, is to have at least two to three different products that you have shortlisted. You start using these products in your environment. You might have to deploy these products if these are only available on-prem or if these are SaaS based. Essentially, you have to integrate them into your current ecosystem so that you are able to test and validate your specific use cases. Using your test data with these products. You validate these products also for user experience, integrations, scale, performance, by sampling real data. Identify the internal customers within your organization who can partner with you with your engineering team and help try using the product and give you feedback. Grade the product based on the experience from these users. And as I mentioned, this is going to be one of the most lindiest phases in the build versus buy analysis. So you have to think about using checkpoints so that you're able to report the results to the leadership. Also, you're able to also at any given point in time, you know, call off the POC for a given product simply because let's say it does not meet one of the critical use cases that you need to support. Also, you're able to also share the results throughout during these checkpoints with the vendor. So the vendors are also able to resolve issues that you might encounter and not just do it towards the end. So now, you know, by this stage, we have pretty much completed the assessment. You know, we have probably framed an opinion, you know, we've collected a lot of feedback, a lot of data. Now you can use all the data distiller and then probably frame it into something like a simple matrix. Again, I put a matrix here. Clearly, this is not like, you know, something that you have to stick to, but I just put something here to be able to help you in saying like, you know, these are some of the factors you have to consider. And, you know, you take all the data and then put it together to be able to make a recommendation and use this matrix to communicate to the leadership and make the recommendation to the leadership.  What do you think the time to value is going to be, especially when you build the product? And maybe what is the time to value from vendor one, two and three for being able to get some of the missing critical features that you need? You know, what is the licensing cost? You can use the net present value to be able to estimate the licensing costs for each of the vendors for the current day and also do the same thing for, you know, the build cost, the infra cost, the cost of being able to run the software on prem or in your cloud environment if you were to kind of build and manage this product. Even if you were to go with, you know, the products that are vendor based, you might have to probably install some components, maybe in your local environment. And that is something that you have to account for as part of your infrastructure cost as well. The maintenance cost, the availability SLA integration cost, these are some of the key attributes that you evaluate the products and your internal build product for as well. You can come back with, you know, a recommendation and you can use something like this to be able to have a discussion with your leadership with your recommendation. So, you know, we, you know, let's say we have made a recommendation.   It doesn't necessarily have to be a build or a buy. It could be many other possible options. One is you may decide based on your analysis that you probably are better off partnering with an open source product that's out there, or you might want to kind of initiate an open source product project to be able to build these capabilities out. Maybe it's because you realize that there doesn't seem to be, you know, a viable option out there. And the internal build from scratch, maybe two time consuming or getting talent is going to be an issue. You may also notice that this is a widely accepted problem in the industry and there's no strong vendor in the market. So you may decide to hence partner with the open source community so that you're able to get the mind share and you're able to accelerate and get the capabilities out. The other is, you know, you may decide to actually use only a subset of features from a product that you think is the best fit. And you might decide to, you know, build the remaining features out internally simply because it could be that, you know, trying to build these features is probably, you know, far more easier and probably, you know, it's also probably going to be too expensive for you to license the entire product because maybe there are scale issues for the remaining areas that you might have to end up paying a huge licensing cost, right? So these are some of the things that you have to think about as options based on the data that you have collected and doesn't necessarily have to be a build versus buy. There are also other interesting options as I think I mentioned in the early part of the presentation.  And as a result, you're able to build and own this internally. Typically, this is a case where you're trying to solve a problem which is mission critical. You want to differentiate yourselves from the competition as quickly as possible. And the problem can be addressed by, you know, identifying maybe a vendor is small enough and acquiring the vendor so that one, you're able to kind of leapfrog because you got some of these capabilities that have been already built and then you can extend on top of it. And as a result, you might reduce the time to value at the same time get some critical functionality in that is going to help your organization differentiate. There's definitely going to be a cost to it when you think about acquiring vendors, especially for talent. You do pay a potentially high price for acquiring a vendor, but maybe the cost is well worth it. And that's something that you have to explore as part of your analysis as a product manager. So now we pretty much come to like the end of this webinar. So let me recap what we discussed so far. One, the build versus buy is a decision making framework. It helps organizations to best determine how best to use their engineering resources. You know, if they want to actually dedicate their internal talent engineering resources to solve this given problem, or should they actually buy a product. It includes various spaces and some need strong engineering partnership as we discussed. The outcome or the recommendations that you can make don't have to be necessarily binary. And by the way, communicating this early that these are all possible options will also help you in getting the support and the buy-in from engineering as well, because they don't see it to be just a buy versus build, but there are many possibilities. Also, the open source option is going to be pretty attractive for internal engineering team. So the outcome will vary, as I mentioned, it varies. In fact, for the same business problem, if you give it to two different organizations, it will vary based on their internal culture, their bias towards build versus buy. It also varies based on, you know, the maturity of the company, where they are within the growth cycle, and could also vary based on the problem that you're trying to address. So there are factors, main factors to be considered as part of your analysis.  And then also look at it in terms of, you know, other aspects such as the operational cost, the infrastructure cost, the maintenance cost, in addition to the licensing cost and the cost to be able to build the solution. One other factor to consider as you go through this whole process is to look at how confident are you that the organization can build this capability out. So that is something you also look at, or how confident are you that you can actually hire, you know, engineering talent to be able to build this capability out, and that's another aspect to look at as well. So I want to end this webinar with a nice quote that I had read on Medium. You know, it's like the bill versus buy discussion is a sign of a healthy organization. It really indicates that the people are thinking about the best way to be able to advance their business, right? So because you're taking all these factors into consideration, and before you kind of dive in to be able to just go solve the problem, you're, you know, considering various factors and deciding what is best for your organization. That is something that is important. Essentially, you're looking at the opportunity cost here for your engineering team, whether they should be actually solving this particular problem or you're probably better off buying a product and integrating that into your environment. So that concludes this webinar. And thank you all for for joining and listening. And I'll be on a live chat on the 18th of April at 2.30pm Pacific Standard Time. Please bring in your questions and I'll be happy to answer them for you. And I'll be, you know, I'll be seeing you all there, you know, during the live chat session. Thank you for attending this webinar again. I hope this has been useful to you. Thanks.