 Hello everyone. So on behalf of Co-Chair Rama and the David, I'm going to talk a little bit about maturity metrics for consistent data quality reading. I like to start with definition of qualities. I'm going to talk very fast if I need to slow down, meaning that you know. So the quality can be a distinctive attribute or characteristic possessed by something, how good or better thing is, or it can be the degree of equivalence of something. So for this talk, the quality includes both data and information quality. There are increased demands for quantifiably assessed information and the data quality, but we are also facing many challenges. Two main challenges are the fact there are many data quality attributes and there also tend to be multi-perspectives and multi-dimensional. I'll talk a little bit in the next few slides. And at the same time, there's currently no community standards for measuring and presenting quality readings. How many data quality attributes you would ask by based on one and a strong 1996 paper and they did a survey with the data consumers, at least they are 179 quality attributes. And many of them are overlapping and I have a list here regarding expertise, correctness, and validated position, free from bias, et cetera, et cetera. You've got the idea. From these 179 attributes, one and a strong select 15 based on the importance of quality to data consumers and they categorize into four dimensions, intrinsic, contextual, representational, accessibility. One can also looking at the data quality based on different data product lifecycle stages. And as Jiaxin has mentioned in Rama et al. 2017 IQC paper in digital library magazine and we have categorized data and the information quality into four-dimension wide science quality during the defining, developing, validating stages and product quality during producing, assessing, delivering stages, storeship during maintaining, preserving, and disseminating stages. I saw as a service quality for data use, user support, and services. So can we qualitatively assess data and information quality? The answer is yes. There are many different approaches, but maturity assessment model in a form of a metric, it's gaining momentum. They help us reduce the degrees of freedom and they provide some structure and also allow for consistent purishing of data quality readings. Some terminology maturity is the quality of state of being mature. Maturity metrics based on back at all 2009 paper can be defined as a maturity assessment model with desired involution in progressive stages from a more ad hoc approach to a more managed process. So one can develop maturity metrics based on the different data product lifecycle stages and they are some efforts going on in various organizations. Excuse me. Those maturity metrics help us to ensure data product and information are scientifically sound and utilized, fully documented and transparent, well-preserved and integrated and readily attendable and usable. And I have a list of some of the maturity metrics developed so far in the slides and also summarized in my overview paper in Data Science Journal. I will now go through all of them, but I would like to touch on just one storage maturity metrics, which I have participated in developing and implementing in the next few slides. So the data storage maturity metric assured for DSMM is the unifying framework for measuring storage practice applied to individual data products. It was developed jointly by a domain subject matter expert such as those in data management science and the technology and leveraging institutional knowledge and community-based practices and standards. And it was evaluated to use case studies with a data set of the different data types or data sets managed by different organizations in collaboration with the CEI Data Storeship Division and ETH Data Storeship Community. And it evaluates storage maturity in nine key components, including data quality assurance, data quality control monitoring, as well as the documenting the practices in data quality assessment. I have a set of introduction slides at tinyurl.com slash DSMM intro. So it has been applied to over 800 NOAA data sets, and it's not a part of NOAA one-stop process, one-stop ready process. The development and implementing the DSMM has been viewed as a positive factor in the NOAA NOAA audit, and it has been adapted by international data management storeship entities such as the working group on information system and services, as well as the WMO International Expert Group for data management for climate data modernization. One of the main challenges for evaluating data quality, data and information quality is that it's the fact the documentations are a lot of the information are not available, and even if they're available, they tend to not be in a consistent way. So the evaluation tends to be tedious and a manual intensive. It probably be okay if one needs to just assess a few or 10 data sets, but if it's not scalable, if we were to do 10,000 data sets to streamline the assessment process once NOAA one-stop metadata content, a metadata team has developed data storage and maturity question here and implemented in a web form. And I want to just touch really quick, one-stop project also developed the best practice for concept ISO quality metadata encoding, and they have ratings being displayed on the one-stop website and results are used for search relevancy. And we also create the setable human and machine-readable maturity report document. The end-to-end practice, the application practice are captured in a paper submitted to Data Science Journal. To summarize a takeaway message in assessing data product or data set quality should be treated as a multi-dimensional problem, and maturity assessment models can be used to measure and present quality ratings of individual data sets. And I think it's time for community to come together for a standardized approach, which will help us with improved usability and interoperability of quality ratings. Thank you very much for the opportunity, and any questions? I will stop here for the question, and if you would like to collaborate with me or with IQC, please contact me at G-Pain at NCSU.edu. I do have a several red caps slides at the end of this presentation. If you earn that max, the current maturity models into the four dimensions that yes, I've mentioned. Thank you.