 I'm going to close up here of the unit by talking about data management and hopefully some of the previous speakers have shown you how data can be very interesting, very exciting. I also want to try to convince you one more thing. The working with data is also very sexy, right? Sexiest job, not just of the year or the decade, but of the century, see? So in any case, one of the authors of that article has recently been named the first US chief data scientist just a couple of months ago. So there's data coming from the White House, people talking about data and funding agencies, our partners, many people today have talked about data. It's everywhere. And it should be. Data management should be woven into every course in science as one of the foundations of knowledge. This is from an editorial recently. If you're talking about data, as we've already seen some examples today, NCAR and UCAR is a great place to be. Here's a couple of examples of what pages from around the organization groups provide data or provide data services or tools, and again, we've seen some examples already. If anything, the issue is not that we don't have data, we have so much data, we don't always know where it is or who has data and what parts of the organization. So this is maybe the best page we have right now that lists some of the services. But if you've looked at this page, it's certainly not comprehensive. In fact, it might even be sort of confusing to a user who's coming in and looking at this and saying, where should I actually go to find a certain data set? So what about a different idea? And there's a typo there. But data.ucar.edu might be as simple as this. This is a mock-up, but it might be something just like this, single front door to all these various services. This is a vision. This is what we're going to talk about and what we're going to work towards. There's lots of examples out there of groups that are working on this type of thing, data.nasa.gov and so on. And we're not a federal agency and don't have the mandates they do. But we see these types of services driving user expectations for us and we work with many of these organizations. So these are some of the benefits, right, simplifying the data discovery of these various services across the organization, easier coordination internally and externally with the many partners, including some on the previous slide. So this is what we're working on in the D-set, Data Stewardship Engineering Team. This is a group that's been working for about the past, I think, eight months with great support from the NCAR Director's Office. And I'll show a list of the members of the team at the end of the slides. And so we're working towards that vision kind of an ongoing basis, meeting monthly. So here are a couple of things we're working on now. The first question is just what do we have? So we're doing an inventory of all community-based digital assets and then assessing the feasibility of getting to that vision of a data.ucar.edu, both technically and then sort of what is the organizational, kind of how does that work? One of the big issues is just the data, even me saying that now, there's lots more than just data. There's software, lots of models we produce, services based on data, publications, et cetera. We're focusing on things that are for scientific community use, so not necessarily everything that everybody produces. So we've done a survey, like I said, it's still open actually for more contributions. 97 responses as of a couple of months ago, so it's certainly a little bit higher now. And I'm going to talk about a few results from that, just to kind of give an example. And those 97 responses are not individual data sets. They're collections potentially of hundreds or thousands of data sets in some cases. So this gives you an idea of the asset types. So as I said, lots of data, we produce lots of software, models, publications, and people could click more than one, which is why the numbers are higher. The good news is most of this that we've gotten on inventory is already publicly available. It's already on the web. So really the question is how do we connect these pieces? How do we cut across them through some common discovery layer, some interface that might sort of point to all of these different things? So the challenge is, of course, working on metadata. If you're doing this kind of a task, there has to be good metadata. And there are some groups that feel our assets should not be included, and there are good reasons for that. I wish I could talk about if you're interested at the end of the talk. And so this is another challenge, different users. And we've already, again, heard many talks today about different users. How do we design a system that provides support to these groups that are already using our services, but through sort of a more common way so they can discover more than just what they already know about? So this is what we're working on, the technical aspects of it, of course, and then the organizational, right, the handshakes of what are the rules of the road about who's able to participate and who should be participating, contributions, and so on, and how to do this in a sustainable way. So that's the vision. And this is not intended to replace what's already going on. It's more of a layer on top or the cuts across these various groups that provides sort of a much, much easier way for external folks, even internally, to find what's going on around the organization. And the last slide is the list of people who are working on it so far. And so please contact any of these folks or obviously myself. And we're happy to talk about it more if you're interested in being part of the survey if you haven't already. Again, let us know. We're still sort of seeing that as an ongoing thing. Stop there.