 And I just want to first confirm that everybody can see my slides. Yeah, yes. OK, I know we're short on time, so I'm going to try and breeze through. Peng gave a really nice detailed overview of the data quality information work that we're doing. This is going to be a more zoomed out view. This is going to be in part public service announcement and also part solicitation for collaboration with respect to our ongoing data stewardship and information quality work as we continue moving forward. So thank you again for having us here. This is quite an exciting opportunity to collaborate with all of you over there in Australia and New Zealand. So just a quick overview of our team. Unfortunately, Yaxing couldn't join us today. But Yaxing Wei from the Oak Ridge National Laboratory, DAC, is our chair of the cluster. The rest of us are the co-chairs. As you mentioned, yes, I am from Jet Propulsion Laboratory. The Physical Oceanography DAC is the primary project that I'm working on as the data publication team lead. I've been doing that for about 13 years now. So I really appreciate the work that Paul is doing with IMAAS, with Oceanographic data. Oceanographic data is near and dear to my heart with what we support over at PODAC. We also have with us today on the call, we have Rama representing SSAI and formerly with ESDIS, continuing to support ESDIS through the Goddard Space Flight Center as a contractor. And so Rama is the former chair of the IQC when it formed in 2015. And by the way, I just want to quickly point out, this is where you can get to our weekly page for the cluster for more information, as well as past presentations that we've published and papers that we published as well. And also I want to point out that we do have monthly teleconferences that take place every fourth Tuesday of every month. And so all are welcome to join in. Be part of our mailing list as well. There's more details on that on our weekly page as well. So our vision is to become internationally recognized as an authoritative and responsive information resource for guiding the implementation of data quality standards and best practices for the science data systems, data sets and data slash metadata dissemination services. So we're here to really just share all the different experiences from all of our cross domain areas of expertise. Some of us are data scientists. Some of us are data managers and users, fundamental researchers. And not all of us come from the US. We have some internationally based people as well. So we welcome that level of collaboration, both nationally and internationally. We do have, I would say, our primary presence is within the NASA and NOAA domains. We have some involvement with USGS, NSF and some others. We also have, I mentioned the invited speakers at our monthly teleconferences as well. But we also have sessions and presentations with which we're organizing sessions actively through the auspices of AGU, the American Geological Society, or AMS, ESIP, and our sister organization, the E2SIP, based out of Australia, OGC, as well as some others. And here's, again, another reminder of where to get information from our weekly page. We have a variety of publications, which I'll try to go through really quickly here. The first of which was published in 2016 by Pang et al., entitled Scientific Stewardship in the Open Data and Big Data Era, Roles and Responsibilities of Stewards and Other Major Product Stakeholders. And we also have a paper the year later in 2017 published by Rama et al., Ensuring and Improving Information Quality for Earth Science Data Products. And then we have the paper published by myself with 18 other co-authors, which we'll talk about in a moment, Understanding the Various Perspectives of Earth Science Observational Data Uncertainty. Going to a quick summary of the Pang et al. 2016 paper, I'll start off by saying that while this focuses on the roles and responsibilities of data producers and scientific data stewards, by no means are we excluding other stakeholders. So we obviously discuss and feature in our discussions, stakeholders representing end users, applications, folks, decision makers and fundamental researchers, all of which feed the information back into the whole life cycle of data quality information. But here where the Pang et al. paper is capturing on the roles and responsibilities are really the primary initial drivers and kind of the sustainers and maintainers of data quality information. And so the front loading and heavy lift initially is from the data producer side in defining and documenting product requirements, as well as the initial data quality screening and assessments that take place for validation verification, ensuring data integrity, product characterization, algorithm assessments and so forth. And then also on the data stewardship side, getting more to where the data is being disseminated, the information is disseminated, assessed, curated, as well as ensuring that it's usable and that all information from the user community feeds back to that continuous loop process. And the end goal of all of these combined is to ensure that there's transparency, traceability, machine readability, that it's humanly understandable that the products produced for data quality information are descriptive and lending all together to a higher quality product in the end. As Pang mentioned in her talk, there are multiple stages of the data product lifecycle. I'm not going to go into detail here, only to say that Pang has already addressed this quite well in her talk, but if you want to learn more about this, you can reference this Rahm et al. 2017 D-Lib magazine paper that goes into more detail. Also to mention that the lifecycle aspect of this is still being fleshed out and formalized through the paper that Pang is publishing through the data quality information guidelines that would be published later this year. Getting to the white paper on the Earth Science Observational Data Uncertainty. So there were, as I mentioned, 19 contributing authors, including myself, and also representing multiple countries. So we had Ivana Ivanova representing among the audience listening in today was one of our co-authors, and actually Ivana is contributing to some follow-on work as well with us. So we're grateful to have that participation from Australia. We also had some participants from the UK involved as well. And really the primary purpose of this paper that was published in 2019 was focusing on the discovery of the various approaches. I make sure that I'm careful and say that these are not recommendations but really more just kind of a synopsis of the variety of approaches taken with respect to the capture, the derivation, and the dissemination and utilization of Earth Science Observational Uncertainty information. And so we're looking at this from a variety of perspectives including the mathematical approaches which are more of the foundational basis mathematically and statistically into how this information is quantified and characterized. And then the programmatic aspects is dealing more with the policies and the strategic programs that are in place to sustain these things moving forward and to build upon what's already been done. And then the user perspective is dealing with how this information is being interpreted and utilized, how it's being applied in fundamental research. On the observational side, this is really capturing more a variety of use cases with which uncertainty information is extracted from real observations. Primarily, we looked at this from the lens of satellite-based remote sensing. We did touch a little bit on in situ and a little bit on data assimilation and modeling but not much. And so because of that, we have some follow-on papers that will be covering that in more detail. But ultimately we were able to identify commonality and differences between these perspectives and there's a number of opportunities that have been identified for the ITC to continue to facilitate and innovate with a number of future opportunities. And I did touch on the fact that numerical modeling was considered but excluded out of scope for this paper to prioritize our focus on observational data. So going on to some ongoing work with respect to the uncertainty aspects, we have use cases being explored for suborbital in situ modeling and data assimilation. And that will come first, followed by a part three paper which is the harmonization best practice and recommendations that includes all of the previous scopes that we've already been able to cover. And Peng has already addressed the ongoing work with respect to the community guidelines for fair data quality information. We have passed as well as ongoing collaborations with respect to E2-SIP, the OGC Data Quality Working Group as well as the Group on Earth Observations or the Geo Data Working Group or EWG. And we have collaborations also within E2-SIP itself with respect to the Data Stewardship Committee that's kind of overseeing the activities of all the different clusters with respect to Data Stewardship and Data Management. We also have very specific clusters such as the data, I'm sorry, the disaster lifecycle and discovery cluster. There's additional ones as well with which we collaborate and interact. And so if you go to this URL here, you'll get a full list of all the different collaboration areas within E2-SIP that are opportunities to collaborate and get more involved with others within E2-SIP and seeing what else is going on. There's a lot going on. What we're also doing right now is we're planning for a summer E2-SIP session to be taking place. I believe it's gonna be in July this year. It will be a virtual conference as well. So we're preparing for that. We're also preparing for a session for the site data con coming up. I believe it's in November of this year. And then we're continuing our monthly telecons as I stated earlier. These are taking place at 6 p.m. UTC every fourth Tuesday. And I have some backup slides as well, but with that, I will conclude. Thank you.