 All right, I think we'll get started. Greetings and thank you all for attending this month's Science Seminar presented by the NSF National Ecological Observatory Network, which is operated by Mattel. Our goal with this monthly series of talks is to build community among researchers at the intersection of ecology, environmental science, and neon. We are excited to have Petina Mendez here today present to us. Before we turn it over to the speaker, a few logistics. We have enabled optional automated closed captioning for today's talk. If you would like to use it, please find the CC button in your menu bar. The webinar will consist of a presentation followed by a question and answer session. As you think of questions throughout the talk, please add them to the Q&A box. We also have a meeting chat. Use this to share links and any other items of interest with the group, but try to add your speaker questions to the Q&A. We will facilitate discussion at the end, and there will also be an opportunity to ask questions over audio if this is what you prefer. Neon welcomes contributions from everyone who shares our values, community, creativity, collaboration, excellence, and appreciation, as outlined in our Neon Code of Conduct. This applies to neon staff as well as anyone participating in a neon event. The full code of conduct is available via a link that I will share in the chat in a moment and also embedded in our Science Seminars webpage. Just down here. This talk will be recorded and made available for later viewing, and will be posted on the Neon Science Seminars webpage probably by the end of this week. Now to compliment our monthly Science Seminars, we host related data-skilled webinars on how to access and use neon data. Registration for those is available on the same Science Seminars webpage at the bottom below the list of talks. For this month, we have a very relevant data-skilled webinar following this presentation on January 23rd. We will have a webinar about how to use aquatic sensor data hosted by Bobby Hensley. So that's something you won't wanna miss if you're interested in our aquatic sensor data. Lastly, if you have ideas for a talk for this seminar series, nominate yourself or a colleague today by filling out the form that's near the top of the Science Seminars webpage right here. Nominate the seminar season. All right, now I'm gonna turn it over to Stephanie Parker to introduce the main speaker. Thanks, Samantha. Today, I'm pleased to introduce Dr. Petina Mendez from UC Berkeley. Tina is teaching faculty in the Department of Environmental Policy, Science and Management. She studies the life histories of benthic macro vertebrates, including working with large biomonitoring data sets and is particularly interested in tricoptor or catasphalyze and their ecology taxonomy and species distributions. Tina's work at UC Berkeley is focused on undergraduate research. She works with about 80 undergraduates per year in the Environmental Sciences major who complete year-long senior theses. Part of this work is mentoring students through the process of finding research questions, learning how to do research and how to communicate science. The focus of Tina's talk today is to bring in some of these themes related to learning to do research and the social dimension of learning. And she'll be talking about the NSF and Society for Freshwater Science Emerge program of which she's a PI. So thank you very much, Tina. Hello, everybody. I am really excited to be here today to give this talk about how our program uses neon data through online collaborative projects. But before I get into talking about the program and the project, I want you to think about how you came to be where you are here today in your career. For me, I can talk about how I came to be interested in insects but for you, it might be geomorphology, LiDAR, data programming or wherever you are today. And I learned how to identify insects and every time I identified something, I really just lit up as every single time I learned how to key out a different organism to family. Think about when you did your first research project or even any sort of first immersive project that you did in school. All of those little bits of light come together as you learn and eventually you came to call yourself a scientist, maybe you came to call yourself a programmer. And with this in mind, what I want to tell you about is a lot about this process and how our program facilitates this process. I'll talk about our goals and eventually come back to this illustration about how we become scientists or how we really become part of identified in our careers. So I'll start by telling you about the Emerge program. The Emerge program grew out of the Society for Freshwater Sciences in STARS program which you can see here on the left. And this program aimed to increase diversity within our scientific society through introducing programming for undergraduates at our annual meeting. This program aimed to build a space for our undergraduate students. And these were really, we wanted to facilitate a space for our attendees from historically underrepresented backgrounds. We wanted to lower barriers to participating in the freshwater sciences and really make space for people to continue to develop a career in this space. Chekko Kohlengoud, who is one of my collaborators on this project, he's really the champion, the heart and soul and a community builder for the INSTARS program. The Emerge program, which you can see over here on the right is an NSF funded program that really expands beyond this just single conference experience from the undergraduates to include graduate students and early career fellows as well. And really the emphasis is that it's a year-round program. So far we've had 67 unique fellows who have participated in our program. The goals of the program are to support scholars in several different dimensions. We really want to support our scholars as learners in the freshwater science. And this key is learners. We are always growing, we are always learning new skills and exciting new topics. We want to support our fellows as team members, practicing the skills of collaboration and I'll get a little bit deeper into that today. And also as community members within the field of freshwater science. And one of the things that I really want to emphasize up front is how it's important to recognize that each fellow in the program is on their own journey in freshwater science. There's no one path that everybody needs to follow. There's a lot of different ways to be part of freshwater sciences. In terms of our fellows, we have fellows from a number of different career stages. Some are undergraduates with interest in freshwater sciences. Some might have some experience in research. Often these participants are aiming to gain more experience, identify the next steps that they can take to keep working in this field. We also have graduate students such as masters and PhD students and these fellows are already starting to, already making a lot of progress in this space, but they're also navigating a particularly challenging space with research and graduate programs and thinking about what happens beyond. We also include early career fellows and these fellows might be entering their first job after they're finishing one of these programs. They might be undergraduates, graduate students, postdocs or even beyond. We always, a lot of us change career jobs and pathways during our career and this might be the place that people need the most support. We call these points where these transitions are occurring, these really are critical transition points. These tend to be these last year or first years in the program where support from a merge may really make the difference in helping a fellow thrive in their program or their new career. And a really interesting thing in our program is that fellows can participate more than once. So if you look at these numbers here, each of these is the cohort that we have a number of different participants in each of the categories for the cohorts. Over the three years, we've had 69 unique fellows, but 18 of those fellows have been in the program in two different cohorts and two of those fellows have been along for the ride with us for three years. You can also see how the balance here breaks out with graduate students really being the main group that we serve through our program. It is where the biggest needs are in terms of support that's really been identified through our applicants. The program is designed to be year round, as I mentioned. And it gives a lot of different touch points and a lot of different types of engagement for our fellows. So as I mentioned, the fellows can be part of the program for multiple years, which means we have these overlapping members between cohorts, which allows you these opportunities for fellows to really be near peer mentors along these career continuums. People can see where they've been and other people can see where they're going. As a result, this makes our program have the ability to be a lot less top down and more connected among our program participants in a traditional model. It means that we have a community of learners and we also have a community of mentors that are interacting in this system. The program components include the SFS annual meeting and a river trip that goes along with that, two workshops with the NEON data, the NEON plus R and the visual communication. We also have communities and online community meetings and online collaborations as well. Our program is organized to have an in-person component that has these multi-day events. So for example, as you can see here, we have the SFS annual meeting and the river float as though like I put a heart here because it's really the piece that like launches and connects each cohort of fellows. It's aimed to build community and get those like sparks going right away and introduce fellows to the larger SFS community, the participants, the faculty, the practitioners, the data managers, everybody is working in this freshwater space. There's also, we also have intensive two to three day workshops that occur twice a year and these workshops are really focused on building skills. These workshops include our programming, visual scientific communication, both of these workshops are designed and led by Dan McGarvey and again, another collaborator and co-PI on this project. The program also includes a component that uses what I can call long form engagement as a learning approach. You can think about this a little bit more like a class or semester long experience. This is in our case, it's a year long experience. And the way that we do this is that we bring our fellows together on Zoom to build community about four times a year for in a little bit more than that, as you can see here in the light blue. And this is where our fellows build connection with freshwater scientists through activities such as interviewing a scientist and we also do programs like career development such as speed mentoring. So this is like building the kind of the personal profile and close connection within our larger freshwater science community. And then at the bottom, we also bring everyone together to do team projects and online collaborations. These online collaborations focus on learning the practices of science and building confidence through engaging with new concepts. It also helps on building collaboration skills that we see are as important to our profession and then developing and doing a lot of different project tasks over the year. We have about six to eight of these meetings per year. And then working with neon data, this is a really exciting part for the seminar series is really infused throughout the whole program. So as you can see here marked with the stars, it's a really big feature of the workshops and also the online collaborations. So as an intro, the part of the program that I lead is the collaborative online projects. We call these the collabs. And so today I'll be talking first about the theory about why this part of the program is important. So this is like the social psychology piece of it. And then also how our fellows are using neon data to learn. And of course, I'm a learner in this space too. I learned so much about doing this work from our fellows and then also in developing these different pieces of the program. So first I wanna talk about a few different concepts in the learning sciences and other research that I think are super important for everyone to understand. And this will help you understand the structure of the program and why we've made a lot of choices that we have and then also why we're using neon data in the way that we are. So I'm going to go back to this idea I introduced earlier. This idea of what it means to be a scientist or for you wherever you are in your career, what it means to become that person in that career for me becoming a scientist. So for example, right now in this neon seminar, I'm largely talking to people of degrees in science or collect data related science, work with the scientific data. So I want to ask you to think, how did you get to the place that you are? How did you become a scientist? And so if you think for a minute, you might have said something like, for me, well, I was inspired when I was, well, you might have said something like, oh, I was inspired when I was young, this experience that I had. I love to be outside. For me, it was like, I love bugs. For you, it might have been taking a cool class in high school or meeting a researcher who was really excited about their work, that infectious enthusiasm for topics. And then maybe you went on to take more classes and gain more experience and to become who you are today. So these different pieces are really great motivations because what we see here is how we get inspired, how we find that desire to do work, to do science, how we learn to do it. But we also have to think that there's a little more to it than just that piece. So what we know from social psychologists such as Mika Estrada is that to become a scientist, that is to be fully integrated in terms of the psychological terms as a scientist, it's more complex than just gaining skills and wanting to be a scientist. There are actually really three dimensions that she outlines to this process. The first is the one you hear at the top, it's developing scientific self-efficacy, which would basically be this self-belief and past evidence that you have the skills of a scientist. You know how to do this work, you've been successful in the past. You also have to develop an identity as a scientist, like calling yourself a scientist. But then you also to really be fully integrated is you have to share the values of the scientific community to which you belong. So when we think about what it is to be a scientist, growing scientists may hit very easily these first two points here, this self-efficacy and identity, especially in already seeing themselves as scientists, but the third one here, this sharing the values of the community to which you belong is really the hard one. So let's go back to yourself. So during this time, you may have also had to figure out how do I fit into this space? Like once you started to try to join or be part of these communities, it might ask your question, is this space a safe place for me to take risks and learn? You might have looked around, you might have said, are there others who share my experience and background? Do I share the values of the community? And then maybe even ask these questions about do I really belong in my part of this community? And what we also know from social psychologists and educational researchers is that we need to be in spaces where there are kindness cues, there are affirmations and micro-affirmations, and these features are especially important for learners from historically underrepresented backgrounds. There have to be cues of kindness and belonging. And I would say that this really extends to probably a lot of people here as well. So what we see here, and when we can conclude from this, is that people need to experience a sense of belonging in the community. We also know that this process takes time and it's not gonna be accomplished in just one workshop or conference. So as an example, in the literature, for undergraduates, you usually need at least a year and a half of mentor research to really stay in staff. So we really need to have these long contacts and lots of it. So this is what we wanna do here is we wanna build spaces where kindness, affirmation and belonging are really at that core of supporting learners in our space. The second piece I wanna bring forward really has to do with the nature of how we do our science. It relates to both the skills of science and some of our values of a scientific community. We often think about research as being independent, but the reality is that a big part of the work we do is collaborative. We work in teams, we talk to colleagues, we share ideas, we make things together. And so what this means is that we need to embrace what Bennett and Gadlin call team science. We need to be united around a scientific goal. We have to develop shared visions. We have to have leadership, communication, self-awareness and trust. But working in teams can really be an uneven experience. We've all had experiences where the teams have been great and other teams have had challenges. And it turns out that there's a lot going on in this process of working in teams. So in 1965, Tuckman, a psychological researcher, developed a theory of group dynamics with four phases. There's the forming stage where this team starts. And then the storming stage, which you can see on the right here of this illustration. This part is where the team starts to develop roles, starts to negotiate about who's doing what. And this is the place where individuals can experience threats, as he says, to status, power and autonomy. And what this means is that some teams never make it past this stage. But some teams, when they have strong leadership and the ability to kind of really be self-aware of this process, can enter the norming stage. People develop trust. People start to settle into their roles and that conflicts reduces. And then finally in the performing stage, these are the teams that just hum, everybody knows what they need to do. It just happens. It's magical and seamless. So given that collaboration is so critically important to our field, one of our goals is to really help our fellows work well in teams. I mean, they need to be able to recognize parts of this process and all of us to develop this team member and leadership skills. So this brings us to the collaborative online projects, which are year-round part of our program. We meet monthly on Zoom. We move through a different part of the research process at each meeting. And in between each of these sessions, which you can see here in yellow, we will have what we call asynchronous tasks where teams will work together toward a specific goal. So you can think about this as fellows going through the research process and miniature and practicing through these projects. So one of the goals of this, the big goal of this collaborative online projects, part of our program is for the teams to develop a project and answer questions using neon data. It looks a little different from year-to-year, how I rearrange things, but it is all about developing a common language, a common way of communicating and understanding what's in papers and the logical structures, how projects look, how they're designed. I've arranged the teams in a number of different ways over the years, but I'll talk a little bit more about that later or in question Q and A. So for the individual collaborations, we really use several different tools for collaboration to help the fellows coordinate, communicate and learn together. So in the teaching world, we talk about how important it is for learners to externalize what they know. So you're trying to get everything out of your heads, you can see that information, you can organize it and make sense of it. And a tool that I love to use is Draw.io. And this allows, as you can see, there's a little example map here on the left-hand side of this slide, allows us to do real-time visual collaboration using symbols, text, color, images, lines and arrows. We also use a number of Google apps for management and communication. And then on the right for the asynchronous task, we use Zoom quite a bit, just like we're using now, fellows will meet on Zoom and then record a seven minute video to share the outcomes of their tasks on Flipgrid, which is just a really neat program where people can post videos, learn from each other, comment and chat. And the neat thing is because we have these overlapping cohorts, it actually creates this really nice archive of the material. So created by the, the materials created by the fellows. So this is what a page might look at like for our monthly meeting. We have an active Zoom session and then we also record our interactive Zoom session in case fellows can't make it so they can catch up so teams can really stay synchronized. One of the big early focuses that I'll talk about now has been learning to develop concept maps using the published papers. And we really relied a lot on neon papers for this. And you can think about concept maps as flow charts linking these boxes and ideas. There's no right way to do these. These can be very like exploratory for people, but it's really about visualizing and organizing information. We can also use them for communication as well. And I'll show you what that looks like. But before I wanna talk about these concept maps, I wanna show a little bit about the, what we're using some of the neon data resources for. So we've been using these concept maps as a tool to introduce published papers about neon or using data neon or papers that use neon data for many of the aquatic stream sites. And the way I identify the papers that we use is using the amazing neon Zotero resource and Steph's gonna drop that in the chat if she hasn't already. And what I do is I go through and identify really all the papers that loosely have to do with freshwater science or primarily ecology or sampling related to these aquatic sites. And these papers become the potential papers for fellows to cover. And these papers also help the fellows learn about the science, how the data are being used and served as models. So when I went through the database as of I think it was last August, this past August in 2023, there are about 82 papers that really loosely have to do with freshwater ecology in the database. And that's about 13% of the total papers in the Zotero resource. Over the past three years, fellows have made 41 maps for papers that covered 34 of these papers. Some of them we covered more than once. And 27 of those maps were really for 19 of the neon papers. So we've covered papers beyond just the neon resource. But I think what I wanna emphasize here is that you really can see the footprint of the aquatic papers in the neon resource that people are using the neon aquatic data and those sampling sites. So there are four main papers that we use to cover the big ideas of what we can do with neon data in the aquatic sciences. So as an example in our second year of the program, we use the Goodman paper as an introductory paper for all teams to help teams explore the potential avenues for their cloud projects. And again, each year more papers come out and we add them to these resources as well. And then four other papers focus on the sampling design data collection and really how to work with some of the sensor data. And as Samantha mentioned, there will be a workshop on some of the sensor data coming up, so keep an eye out for that. Those are great workshops I've participated. Other papers we cover include exploration of the physical or environmental data, biodiversity and food webs, community stability, feeding ecology and body size relationships. And these papers really serve as examples for how do researchers develop research questions? And these examples can be used for collaborative projects. So before I show you some of the details of these maps, I wanna show a really zoomed out version of a map. And as you can see here, many of these concept maps are really large. And I'm talking like poster size once you really get to it because the neat thing about dry oil is you can scroll around and zoom in and zoom out. So in this example, four fellows worked on it together and you can see the different regions for the intro, methods, results and discussion and even a little map of all the sites that each fellow took responsibility for. And so here's a different paper. Here's a zoomed in example from the Hensley paper that talks about the different designs for in-stream sensors. So in this section, you can see how the fellows have represented the different designs included in the image for the paper. And they've also outlined the data collection and the data processing. So if you look at this in a snapshot, like you could read the whole paper or you could really understand at a high level what is going on and how these sensor designs work, what are the choices that were made by the researchers. Fellows also will dig deeper into the paper to examine this internal structure related to the logic of the data collection and the evidence that the authors are collecting and using. So as an example here on the top half, the team that did the concept map for the Jarsna paper was able to pull the paper apart into these logical sub-question units and pull the data from the methods and results sections to link across. So as you can see here, they identified the question, the different data sets and their characteristics, the metrics and the findings and below they did it again with the next sub-question. I've covered up a little bit of it, so I apologize for that. Because again, these things are huge. In terms of organizing information, sometimes the fellows will extract information and bring it back together to create new ways of consolidating that textual info. So for example, when covering the lead paper, fellows made this really nice like table graphic that covered the aquatic taxa. The original information looked like this in the text and there was a section for each of these different tax. I think there were even more taxing than this and they've basically boiled it down. And this is neat because it really can serve as a resource for other teams at a really high level. And it's neat because I don't tell people how to do this, they make it happen, they learn from each other, they share, it's amazing. After making the maps, fellows will share out the paper in a seven minute video on Flipgrid and the concept map serves as that platform of the visual. I find it to be a really nice departure from Google slides with kind of like layers of information and this allows this movement. And an important point that I wanna emphasize here is that the process of making the maps and sharing the maps is the place where the learning occurs. It's in organizing that information, representing it, asking questions, explaining it to each other and then even thinking about packaging it to share out to the other fellows in the program. And one thing that should make clear is I don't expect fellows to make concept maps for every paper that they ever read in the future. I mean, nobody has time for that, but the practice of doing it for even just a few papers helps us to see the structure for future papers. So when you read a paper in the future, you can see those internal structures, you start to make sense of it, makes us better readers, makes us better understanders and consumers of the science, and it also makes us better designers of projects and even better writers and scientific communicators. So it's really bringing all these pieces together. We've also used the papers for generating collaborative project ideas. Here's fellows sharing a Flipgrid video that digs into the big ideas in the Goodman paper, the opportunities that were laid out and what they would need to learn to be able to do a project. So what questions come out of like, how are we gonna ask some of these questions? How are we gonna answer them? In the first year of our program, we also had fellows read papers and then find the neon data product and the data that the authors used to take a look at what does that data look like when it kind of comes in? Some of the teams did really neat stuff. They made some quick visualizations of this data that they downloaded. And here's a team sharing that piece there. In year one, we also did exercises where we annotated and made flow charts for the workshop R code. And what this allowed us to do was help everyone take time to understand the code, to make flow charts of those data analysis procedures and see the structure underlying code and really serve as a learning resource. And I didn't do it the second year, but one of the fellows commented on their evals, they're like, why didn't we do that again? So we're probably gonna bring it back this year in some form for our fellows this year. And that's the neat thing is fellows really provide a lot of feedback and structure to the program. We're constantly adapting, we're constantly making it work. And one of the really awesome things I wanna share here is that one of the things we get to see in these sharing videos is these moments where the fellows are teams are smiling and laughing while sharing out some of this really difficult science. And so what it shows us is that like even though this is really hard stuff, people are working together, they're learning, they're connecting, they're building community. It's like really humanizes science because the way to get through science and the way to do all this work is to really be part of it and to take risks and to build a space to learn. So I just love this. I really wanted to share this screenshot. So with that, what I wanna do is transition back to the collab meeting topics to launch into this last part of the talk about how our program is using neon data. And as I mentioned, the collab part of the program sessions and the asynchronous tasks are designed to facilitate this development of these year-long projects that culminate in a digital poster session. And this poster session is largely internal. It's really for us, I will be showing some of these posters and outcomes from it. So what you can see in our flow is how these collabs are trekking their way across the care, but we're also interspersing them with these skills workshops. And those give us a lot of tools that fellows can use throughout the process. And so I really interwoven into the design of the program. For the three-day, our workshops, I'll talk about both the workshops and the collabs here. For the three-day, our data workshops, Dan McGarvey develops a research question based on ecological theory to test using neon data. He designs analysis process, the R code, annotates it and builds like all kinds of really neat pieces for the fellows to use and learn from. He facilitates a really great workshop. And what he is showing here and doing for the program is making visible the part of the process that underlies a lot of the papers that we read, but we never get to see. We never get to see the code that goes under it. We never get to see the decisions that are made or how it connects. And Dan really makes that learning process visible. And he also provides the code and the flow charts and the model to really for the fellows to use as models for their own project. So in our first year, the workshop explored the traits of benthic macroinvertebrates. In year two, the workshop explored length mass relationships of organisms. And then this year, fellows explored in the workshop loose equilibrium theory. And Dan has already given a neon data science talk about some of this work. So hopefully you can find that if you're really interested in that as well. So for collabs, the focus of the collabs has varied over time. In the first year, all of the fellows collaborated on completing work from the traits workshop. So we used Dan's workshop as a launch point. In the second year, I assigned fellows to teams based on their research area. So whatever their interest was at the time, we also assigned early career fellows or senior graduates students to lead these teams and being kind of like a point in this near peer mentoring structure. But one interesting thing that happened in that that we were surprised by is that some of our returning fellows had already become experts in doing some of this emerge work. So they actually took on a lot of these leadership roles as well. So it's just like fantastic to see this happen just in the matter of just a year. So growing on that energy, this year our fellows are fully leading teams, our returning fellows, they're designing projects, they're recruiting for teams and including people. Project projects can go beyond basic research and now we're really letting fellows explore education projects using they on data or even specific demonstrations of skills. And so really kind of getting that practice. So thinking about what are people's motivations? What do they wanna learn? How do they wanna do it? So we're in the next few slides, I'll show some examples of the projects by the fellows over the last two years. In the first year, the project was I mentioned was designed by Dan focusing on traits and trait diversity and each fellow had a site level contribution, a piece of the code. And so like I said, this is a project that was really a great model for what could be done. In the visual scientific communication workshop, Dan built the structures of this so fellows could really all work on posters, learn how to display this information, bring it together to really make an effective poster. And so what you see here is a poster that the fellows presented at the SFS meeting in the summer of 2021, the Joint Aquatic Sciences Meeting. So the rest, after this, the rest of the projects that'll show our projects from year two, which are gonna be much more team oriented. As you can see here, the posters are really big, so I can't show all of them, but I'm kind of cropping them so you can get the big idea of what these look like. So one of our teams focused on just four sites in the Eastern US, so we encourage some teams to like think small, think about just looking at a few sites. And two, they used two data products related to dissolve gases and nitrate in surface water and applied autoregressive integrated moving average modeling on the CO2 values. And they projected fluctuations in those CO2 values for 12 months out from the last data point. You can see some of their graphs here, sites and domains that they used. Another team was motivated by a really timely environmental problem, the series of wildfires that have been occurring over the last few years in California. And they explored a number of data products related to nutrients at the two Sierra Nevada aquatic sites. And so you can see they focused on T Kettle Creek and Upper Big Creek over a few years. And so all kinds of different relationships. This group was really primarily interested in algal blooms. They really wanted to know about the nutrients and how the algae respond. One team took on like a really big ecological theory and they tested the latitudinal diversity gradient theory where diversity, which says that diversity increases from poles to the equator. And they used 24 neon sites and the fish data product. So they focused on just one big taxa group. They also used prism data to evaluate temperature variation as a potential contributing factor. Another team examined the impacts of drought conditions on benthic macroinvertebrate communities in Kansas. And they used just two neon sites to do this. And this team actually had a number of returning fellows. And what they did was they used some of what they learned in the first year's workshop for the coding and analysis for the project. So it was really neat to see this come across. And we also had a PhD student who was using this type of analysis in his research. So you can see here, it's really bringing it all together. And some of what you can see on the top part on most of this poster is some really great design work with color and layout that the fellows did during the visual scientific communication workshop. And then the last team used seven sites and the zooplankton metabar coding data product to explore differences in those zooplankton communities in different regions. And so they used about seven, as I said, seven sites here. And they found that they really clustered by state. And one of the things that you'll notice about this poster here is it's really fun. It's really meant for science communication, not the same as like the big research communication posters. It's really fun, has fabulous illustrations, so cute, a really tight color design. And you can even see how they lined up all the color in the organization as well. So just fantastic in seeing how these come together. So what the collabs show also allow our fellows to practice is navigating this people part of the equation. So learning to work remotely with collaborators, both across the country and beyond the country. We even have a few fellows each year from Africa who stay up really, really late to be part of our program and to work with their teams. And so what this means is that fellows have to navigate technology, time zones, delegating, commitment, external responsibilities, wrestling with the data. Some of our teams have been really successful at making this work. And then others are still learning and are getting better at it and are helping each other out in making these teams work. And again, I think that's like the forming storming, norming, performing piece, like really getting past that hard part. Some exciting directions for the program have come for the fellows and it really involves us making space for fellows to lead. So fellows come to us really wanting to do more behind the scenes work to help build and support the program. And fellows often will say, well, how can I help? I really wanna help. What this has meant is that this inclusion and belonging in the community has really transitioned to fellows helping build that community. So returning fellows are supporting new fellows in teams, they're teaching these techniques and the tools they keep coming across things. I'm like, hey, I haven't covered that yet, but they were already doing it, which is fabulous. And then fellows have formed, also formed a group to provide alumni programming. So they're building a space for the mergers to land once they kind of really kind of finish with what they're learning in the program to keep those connections going and to keep what we're doing over the longer term. Our program also includes data collection by an external evaluator on the different aspects of the Emerge program. And I'd like to share two quotes that really sum up the collabs and the impact of the program. One fellow commented, my understanding of the freshwater science as an arena for collaboration and extensive interpretive activities has been strengthened. By reading papers and finding efficient ways to convey them to audiences, I was able to expand my skills as a communicator. And this was largely influenced and aided by my ability to exchange ideas with my fellow collaborators, which is like fantastic. Another fellow wrote freshwater science is becoming more and more inclusive because of programs like Emerge and Instars. I feel supported and respected, especially as a young and underrepresented student in the aquatic sciences. So you can see that there's like long-term engagement working with other fellows and building like what we consider to be a fairly self safe place to learn and really have a strong impact on individuals. So with that, I'd like to really thank everyone who's been involved in the program so far. We have two more years, two more cohorts we're recruiting right now, especially when I thank all of our fellows for trying new things, building the community, helping each other, talking to us, giving feedback, all of these pieces. I'd like to thank our advisory board and their commitment to the program, especially thanking Stephanie Parker today for inviting me to the seminar and all her for work with Neon and being our point person with all of the freshwater data. And of course NSF for funding, SFS for continued enthusiasm and support, and then SEI for data collection, recording our outcomes for the program. And with that, I'll say thanks and open this up for questions. Thank you very much, Tina. To everybody listening to the webinar, please pop your questions into the Q and A. But I'll start out with a question now. When the fellows are working with Neon data on their own, are there any sort of common sticking points for them, like things that they commonly have trouble with when they're trying to access and work with Neon data? I think the access piece is the easy part, right? Because you guys have all these really great pages, you have all the different ways of browsing the data. I think once you get all the data downloaded, it's a lot and it's all broken up. And so learning those processes are just kind of part of that piece of learning to work with the data. The neat thing is that in Dan's workshop, he actually has a lot of scripts that he's put together. He's using a lot of your different scripts and pieces to do that. And so we have these demonstration piece that help to lower some of those barriers to working with some of that. But I think the hard part is coming up with research questions. That's the really hard part. We have a way to go, Tina, in the Q&A. So I thought I'd share that. Oh, OK. Is there a specific goal of Emerge to use open source data, open source programs, things like that to make it more equitable for students? Yeah, I think that's the big opportunity, right? Even in looking at that figure with the different papers over the years, you can see how open data and having these resources was really impactful to probably people during the COVID pandemic. And I'm sure that's been discussion that a lot of people have had is, well, we're all here. We can't go out and collect data. Like, oh, what are all these resources? What can we find out from what's there? And so we want to have our fellows have access to really well-consistent data because collecting data takes a long time, especially those of us who work with invertebrates. It is so much work to collect, sort, ID, and put it all together that this is just this huge opportunity to ask some of these bigger questions that we can never collect on our own. The other piece is I'm a big fan of just open policies and letting everyone, building team practices that keep everything very open. And so a big emphasis here is we build. Fellows have team folders where they have all the different pieces. Some pieces I wasn't able to talk about is a couple under-the-hood curriculum things. For this year, we're using a personal, I think it's called a personal user manual that each fellow goes through and writes, like, how do I like to work? How does this work? How can you help me? How can I help you? What are our team values? How do we want to work together? All of these different pieces and really making that visible so that you can work together as a team. And then also we're doing a lot of work in terms of building what I call project plans, where it goes from top to bottom, like, what are our motivations for this research question? What are our questions and sub-questions? What data products are we going to use? Where are the files, you know, things like that so everybody can really participate? Because as we know from working in teams, not everybody is able to be there at all times. Like a lot of my collaborative teams that I work in are a little bit asynchronous. But if everybody has all the things, people can still do their job. And I think that's so important because I think that's part of really developing those skills and collaborations that are gonna carry people a long way. We gotta help each other as collaborators and I think that's really important. Great, thanks. A comment from Amy Rosemond in the Q&A in terms of establishing research questions. It has been wonderful to have other neon-based studies for the fellows to read and learn from. It's been a great piece that Tina has included in the collab. So more pubs that explore and use neon data will help build and build momentum. Yeah, I actually had three more pages of pubs that I could share with you all that we've been using. I was really, I couldn't fit everything in and that was one of the pieces that I couldn't share but I'm happy to share our list of papers that we've identified as aquatic. Again, I'd like a really big applaud to the Zotero managers for neon because it's just like phenomenal to be able to just search and see it and see everything that's happening and that effect that it's updated so frequently. Like I think I pull, like there's like 10 new aquatic papers every year. It's great. Yeah. Do you want me to answer that? Oh, do you see it? Sure, are there types of data you wish were more available to work with from neon? I am not sure about the answer. I feel overwhelmed by how much data there is available from neon right now and trying to figure out how to do that. Yeah, that's a really good question. I could ask our fellows. What would you like? Yeah. And I can kind of build people. Yeah, and building on that, I was curious if you have any insight into the center data versus the observational data because they're so different. Do fellows tend to be more drawn to work with one or the other or is one more intuitive or versus more challenging or do, is it just depends on the person and there's uptake of both just depends on the team or do you have any comments on? I think it depends on like the skills on the individual teams or the research areas. Like, so a lot of our graduate students are really interested. They like have a skill that they've been learning in classes and like, I'm going to apply that here. Oh, this is the right kind of data. So like a point I really want to emphasize is that a lot of our projects are like the first time fellows are taking on a data set this big, although we have had a few fellows who are working with data from Neon for their graduate work. And even one of the papers that we read was by a fellow who worked on Neon data for his dissertation. And it was really exciting to have one of the authors like among us that had worked with some of this data. I've almost lost my train of thought there. But yeah, generally, fellows are trying to apply what they've learned in other spaces and then other fellows are trying to stretch, right? And learn these new skills and learn and gain this experience. Like for example, that workshop that Danda in the first year with an MDS, one of the fellows said, oh my gosh, I can use this in my graduate work right now. And then that fellow took that and said, oh, for our research question this year, yeah, let's keep applying this, but look at these other sites. And so we can see how this uptake happens. This next week in our program, this is a little bit of a preview is we have one of our sessions and we're gonna really talk about what are those other resources that you have available at Neon, like the workshops that you're doing, the code repositories and pieces like that. What else is available to help fellows do their projects? So we're at the point right now where our fellows are just, they have just really quarantines have just started laying out and they're really about to start doing the work. And so we're out there. So like really great critical piece for them. So cool to see how foundational the Neon data are to this whole very inspiring effort. I guess one other question I had was Tina, do you feel like this, if Neon didn't exist, could you do this program the way that you're envisioning it? Like is there another open ecological, freshwater sciences repository that you could use instead or is it like you just, like Neon is very necessary to make this whole program work as it's structured or? I think to really make it work, I think this is the big place for it. I would have to look real hard, I think to find something else. The interesting thing is like even some of the projects that the fellows have chosen, like they've chosen sites in very specific places. They don't necessarily like it means something to do this work on drought in Kansas, right? To some of our fellows who live in that area. And so that's really a neat connection because like in order to learn and in order to be invested and motivated, like people have to feel a connection to the work that they're doing. And I think that's what's really neat about some of these sites is that they mean a lot. And then also it's just, I don't know, I think it's been really great and has allowed us to have a lot of space for people to carve out little niches and to do and learn what they wanna do. As I said, you know, this might be the first exposure for fellows in the end, but maybe some of our fellows will develop dissertation or thesis chapters from it, senior projects and so on. And so I think that's part of our hope by introducing people is that this is an introduction that forms this space for people to do deeper engagement and publish work from it or published educational resources or teaches how to do different things within the group. Do you have a sense of how many of the fellows are using neon data for their graduate work? I'm not sure offhand, but in last year's cohort there were at least three or four, I think, yeah. And they might have been involved because their PIs are involved in the aspect of neon, things like that, but yeah. We're watching these things grow. It's kind of experimental in ways of us trying to do long form projects. Like I said, not a lot of programs do that. A lot of it's like two weeks or a summer program. And so this is like, how can we like build long-term collaboration with some of the people? And that's what we're really hope for is that these collaborations exist. And you might have talked about this early on in your talk, the fellows typically stay for three years or is one year more the norm or? It just depends. We have had 19 fellows go from year to year, not like 19 every time, but like probably eight and eight each year. It just depends on what people need at the time. Like some of our fellows are like, wow, I'm starting my graduate program, this would be great. Or, hey, I really want to keep being involved. I'm applying to graduate school. Or, oh, hey, I really enjoyed this. I want to help somebody else with it. Or, oh my gosh, my friends are here. Yeah, I'm going to keep hanging out. And so that's been, I mean, it's a really amazing program for us too, because we get to see people grow. And then we get to grow a lot. And like, how do we do things when we learn? I learned so much from the fellows, so much from the fellows in terms of the work they do and the way they talk about the papers and so on. Well, last chance for questions from the audience. If anyone has anything, drop it in the Q&A box or you could raise your hand. But, barring any more questions, I think it's a good time to end. This was a wonderful presentation. Thank you so much for sharing this program with us, Katina. As we mentioned, on January 23rd, there is going to be a data-skilled webinar on using Neon Aquatic Sensor data. So please join us for that. You can register on our Science Seminars webpage. And then the next monthly Science Seminar will be next February. We're going to hear about using local data to understand ecosystem responses to air pollution. So very different topics, but equally interesting, we hope. So see you then. Thanks again to our speaker and have a great day, everyone.