 Hopefully you can see my slide there. Yes, we can. Thank you, Robin. Yeah, brilliant. Okay, cool. So yeah, Mingfang asked me to just kind of give you a brief presentation related to data governance and its involvement and impact with data quality and how data quality and governance kind of can interrelate and interact. So firstly, I would just like to give an acknowledgement of country. We acknowledge and celebrate the first Australians on whose traditional lands we meet, and we pay our respect to that elders past, present and emerging. And for me today that's the people of the Gadigal nation on the rural lands in Sydney. Just the content for today's presentation I'm just going to give a brief introduction to myself so you know a bit more about me my background and where I can subsequently support or answer any questions people might have with regards to data governance or my current role with the ARDC. I'm going to comment on the importance of understanding what research data is in relation to this sort of information, touch upon data governance and data governance principles, then move into kind of the considerations of data governance and impact for data quality, and then I'll try and answer any questions that people might have. So just a quick bit about me so my background and skills I've got over 12 years experience working with data. Very much kind of within the stem size so sciences, arts and humanities, and the glam sector particularly working with non traditional research, and I've had various roles. In the UK I was the research information manager at Glasgow School of Arts so working very much with non traditional research, before moving to Sydney where I became the digitize repository and digitization manager at Sydney uni, and then senior research and then I moved to Liberia at UNSW, and I have now moved to the ARDC where I'm the research data specialist in data governance, which is a role I'm thoroughly enjoying, and this is an area that I'm happy to support projects and people with in that area. Something I kind of wanted to touch upon is the question of what is research data, and this is actually a common question that comes up for researchers, and they're kind of thinking about the projects that they're doing, and actually understanding whether the data they are collecting can be classified as kind of research data and how it should subsequently be managed recorded understanding say the quality of it and understanding the fairness of the data and how it could be potentially used in the future. So research data primarily is information records and files that are collected or used during the research process, and data maybe numerical descriptive visual raw analysed experimental or observational. So this is quite a brief definition and I must say there's many, many more out there, each of differing lengths, but I quite like this one as it looks quite simple and to the point. And then you have to also look at what types of research data might be out there that you might be kind of working with and needing to understand the quality of that type of data that you're handling. So, primarily we have our quantitative data so numerical information qualitative which very much comes from your surveys focus groups, obviously no data in non traditional work so visual materials, that sort of thing. And very much I've come into contact these days with indigenous data and understanding the quality of that and how it can subsequently be handled accordingly, potentially understanding kind of community aspects to the management of data. You have sensitive data of course, which needs to be handled and tackled in its own ways, software is becoming much more prevalent as a research data type. I've just mentioned these areas all fall under the stem glamour has kind of categories, then kind of understanding the type of data you have whether it's primary so that you've collected firsthand or secondary data that you're actually making use of someone else's data, such that you really need to be aware of what you're using and the quality of that data. There's a quick kind of overview and commentary on data governance. So for me, I see data governance as an umbrella term that can be used to describe rules and policies for the handling and management and sharing of the data created by the research project or within an organization. Organizations have different types of data governance so that could be the data governance associated, say within your university for your kind of HR data and general data around the organization. For this talk, the particular interest is in research data governance so it's the data that's coming out of the research projects. Policies and frameworks tend to be created as part of a governance approach to support data governance, and within these specific principles need to be stated and adhere to. We also mentioned that principles can follow particular structures and there's different options out there when it comes to the principles. And also the policies and frameworks that can be created with regards to data governance can be as detailed as you require them to be. I've experienced with the ARDC and some policies and frameworks I've reviewed for projects. Some of these have been in the realms of 40 to 50 pages while some have just been a couple of pages long. So it's very much what works for you and what's best for the project that you're working on. This diagram just comments on a couple of considerations in association with data governance. So the first concept relates to value. So do you actually know the value of your data and within kind of value of the data you could also be investigating and understanding the actual level and quality of the data that you're going to be handling and subsequently allowing access to which is the next step in the diagram. So do you know who actually has access to the data that you're you've been creating or you're working with. Do you know where your data is actually being housed? Is it on kind of local server? Is it within the state? Or is it external devices? Is it in Australia or outside of Australia? These are considerations that need to be made when thinking about data governance. The security of the data is important. So who's protecting it? Is there any cybersecurity or authentication processes put in place for the handling and sharing and management of the data, which leads on to how well the data is subsequently protected in preparation for reuse. So that's kind of looking at what licensing or conditions, terms and conditions that might be put in place on the data to help support its use. So from the research that I've done since joining the IRDC within data governance, the framework and policy can follow specific principles and listed here are some areas that could be covered. So common ones that have come up in the literature that I've investigated and looked at. So it's very much kind of looking at the integrity of the data that's being managed and collected within the research project and subsequently handled and shared. So this is also where kind of the quality of your data will come into play because it influences the integrity of the data. So transparency is important because this will ensure that the data and information being collected is understandable. It's clear everyone using or potentially using the data is across its purpose. So you've got audibility and accountability stewardship is very much related to who is managing the data so roles and responsibilities in the handling creation of the data sets and subsequently say the sharing so the roles of data custodians and the research is actually asking to gain access to the data that's being created from a research project. It's important also to kind of add checks and balances to the data to ensure it is sufficiently managed. Those standards might be put in place for the management and also risk and change management should be covered within the framework for the handling of data governance. So just some other considerations and support areas that kind of crop up with regards data governance. Projects, areas of studies and research creates unique data sets that need to be managed and governed, governed accordingly. When thinking about the data that you're creating and is going to be the kind of endpoint of the project is important to have a understanding and knowledge around the actual type of data so it's formatted size, whether the data is seen as sensitive, sensitivity of data will impact on various aspects of the management of the data such as the ability to share and license and publish the data. What might the intended audience for the data, are there any caveats or any specific rules or work that needs to be done to ensure your data is of sufficient quality for the specific audience that you're working with. The intended reuse of the data might someone want to reinvent the wheel and do some new research using your data or do they want to actually use your data to verify the results of the work that you've already done. The actual kind of sharing of data needs to be considered so terms and conditions put in place and understanding of where the data might potentially be stored and its access so this kind of relates to local and national storage, or whether it's just kind of computational device that gets used and licensing copyright IP, those sorts of areas need to be understood and attached to the data and this really influences the potential reuse. This data also of course is the actual quality of the data that is being collected or created and you would want to ensure that the quality of your data before sharing is of a sufficient standard that other people wanting to reuse the data can get the benefits from the data as well. Of course, fair and the fair principles are very common when it comes to data management and handling of data, so is your data at the end of your project can it be classified as fair is it findable is it accessible is is it interoperable and is it reusable. Following on from kind of looking at these considerations this really helps you to develop various principles and the policy for data governance and very much is this can be done through the development kind of a checklist to allow people to really understand if you've gained enough knowledge around the data that's being collected as part of your project. So just coming to the end of the presentation now so just a couple of comments related to kind of data governance and data quality. So, in my opinion, data quality will impact components of data governance, particularly relating to the actual sharing of the data. I do not want to share data with another individual organization if they're not happy with the quality of the end product and the data that they're wanting to share. Data quality will also impact an influence request for data so those kind of searching for data will will themselves be looking for good quality data. When doing their literature reviews and their searches for supporting materials for their research. It's also important to have reliability and trust in the project so quality assurance and when looking at other people's work and other projects to really see and understand where the data can subsequently be used for other projects. And as mentioned with one of the principles that I highlighted earlier to do stewardship roles and responsibilities impact the quality of the data that might be being created as part of the project and will subsequently feed into the data governance frameworks. So who is handling the data, who is requesting the data, and not making this an over complicated process. And then as mentioned earlier, the fairness of the data, and whether the data fits in with these principles of findable accessible interoperable and reusable. So based in conclusion just a concluding statement here. So data governance is a fundamental component of any research project. It is important to have a governance framework in place to support the management handling and sharing of the data created by project. And it is important to ensure that any data that has been created as part of the project is a good quality to support sharing and future reuse of the data. I would like to end my presentation there and I will stop sharing my slides and Catherine, you can stop the recording.