 Climatic uncertainty and a growing human population heighten the need to understand global availability of drinking water, a resource most readily accessed in surface waters of the world. Now fortunately, the data necessary to identify surface water patterns have never been more abundant. And in particular, high resolution data, capable of addressing questions related to water abundance at local and global scales are becoming more and more common. And more complicated. Effectively working with remote sensing, derived model products and in situ data individually can be a challenge in and of itself, but unifying these disparate data sources can require specialized skill sets and access to high performance computing, both of which are tools that can be just out of reach for many researchers. So to make these data accessible to researchers and managers with a range of data wrangling skill sets, we created the unified global lake area, climate and population data set or GLCP. By bringing together remote sensing and climate reanalysis products, the GLCP consists of annual lake area, basin air temperature, precipitation and human population for over 1.42 million lakes globally from 1995 through 2015. Creating the GLCP required a workflow that relied heavily on the Google Earth Engine in tandem with local high performance computing infrastructures. And if you're interested in how we created the data set, you can access our scientific data paper via the QR code. However, the main takeaway is we put in the grunt work of creating and validating the data set so you don't have to. And we envision future users repurposing the GLCP for many research questions. So to prime those thoughts, especially for users who have never thought of using these types of data before, we detail a few case studies in our L&O bulletin piece which are more focused on how natural resource managers can use the GLCP. Let's walk through one of those now. So take for example, Buffalo Lake located in North Central Washington State. Buffalo Lake is an oligotrophic lake characterized by steep littoral drop offs. And it's this littoral zone that could be crucial for creating fish habitat. Now, monitoring efforts at Buffalo Lake keep track of fish population stock estimates. And they may be interested in knowing how Buffalo Lake's surface area compares to other lakes in Washington State. And they also may be interested in pairing a time series of Buffalo Lake's area within situ chemical and biological data. Now, luckily with the GLCP, managers can address both of these questions. First, to compare Buffalo Lake with other lakes in Washington, they can take a page out of Amina Pollard's playbook at the US EPA's National Lake Assessment by isolating all lakes within Washington, creating a distribution of lake area sizes, and then seeing where Buffalo Lake falls in this distribution, here represented by the vertical dotted line. So in our case, Buffalo Lake is generally representative of larger lakes in the state. And it's with this information that managers can create a list of other lakes that may be more representative of Buffalo Lake based on its area. Second, managers can create a time series of Buffalo Lake's surface area in terms of seasonal, permanent, and total water, which could then be paired with co-located biological data. Now, we recognize that some potential users may not have advanced data wrangling skill sets. And the thought of working with a larger data set may be intimidating. So Matt Priscilla, one of the data set's co-authors, will walk through a brief live coding demo of what it's like to work with the GLCP. You can get started working with the GLCP data with very few modifications. To start, I've already loaded the packages I need and the data set itself into R. My next step is to filter for the High Lake AD of the lake I'm interested in. You can find this in the Hydro Lakes data set or by filtering based on latitude and longitude within the GLCP itself. Now I can immediately plot a time series of the total surface area and square kilometers of my lake of choice, Buffalo Lake. I do so by referencing the total kilometer squared column. If I wanna add additional columns of data to my figure, I can reference these columns as well and add them in. Lastly, I'll add in a legend to note which colors match up with which variables that I've included from the GLCP data set. Thanks, Matt. Finally, if you're interested in reproducing or tailoring any of the examples we present in our Eleanor Bulletin piece, you can access each of the scripts in our public GitHub repository via the QR code. Moving forward, we envision many potential uses for the GLCP, from global scale analyses to applied local questions. Now we have striven to make interacting with the GLCP as user-friendly as possible, but if you do have any questions, please feel free to reach out over email or Twitter. Thank you.