 All right, let me know when you can see that. Can you see it? I can see it. OK, I'm going to go into presentation mode. Just make sure I'm not seeing the preview. Are you seeing the preview of the presentation? I'm seeing the presentation. All good. Excellent. OK, thank you, everyone, for your patience with this. This is one of the perils of presenting virtually in Ruth and I are in two different places. But we are really excited to be able to share some of the explorations we've been doing in this area of seeking to co-create inclusive vocabularies and to identify ways of standardizing practices in relation to data use. Before we begin, on behalf of Ruth and myself, we just wanted to add our acknowledgments and pay our respects to the traditional custodians of the lands on which we are all meeting and working. In my case, I'm sitting on Gaia Moguland. And I'm on Kamaraguland. And so we would also like to extend that respect to any First Nations participants in this symposium or any First Nations people listening to the recordings at a later date. Just because thematically what we're really looking at are the issues of the value and the necessity of social engagement. I love this particular representation of dream songlines and tracks in Australia, which builds a map of Australia, not topographically, but through the connections of one person, one community to another over time. So that is, if you like, one of the themes that hopefully we'll be trying to show in practical ways in what we're talking about today. So what we're talking about is the necessity of working towards a common language. And I think this cartoon kind of says it all so often, the challenge of being explicit. And I think Rob did a really lovely job of pointing out what I often call making the invisible visible, this challenge that we have in terms of getting people to appreciate that to get from A to B. There's a real, there's an awful lot of work that sometimes needs to be made more visible. This is particularly true, as the examples have been shown in today's presentations and in yesterday's, this applies especially to communities that are seeking to link data sets at the global level. So Ruth and I are both working in ISO standards challenges dealing with data use, ethics and the idea of standardizing trustworthiness as part of JTC one, which is a technical and communications aspect of ISO. I'm also a part of the World Fair Projects, the Solar Ball Global Health Urban Health case study in particular, I've been associated with co-data for a few years now dealing with data ethics and resilient cities. So, can you still see me? My connection just suddenly dropped. That's the power of the machine when you're talking ethics, it doesn't like it. But what we see this as is a social issue in terms of the data interoperability. And it is particularly, as I wanted to show in this example on the next slide, it is a socio-technical challenge. It's one where, Ruth, if you're able to advance. So there, okay. Categorizations and classifications have political power. They have ideological power as well as cognitive power. And this example, I think shows just the challenge in terms of what words you use, how you use them and where they are placed in any sort of vocabulary. So this paper references the debate around the naming of chronic fatigue syndrome and classifying it as either a medical or a psychological or mental health concern. That has profound consequences, not just for the person being named and labeled in that way. It has profound consequences for how information might be found, how a person looking for treatment would be dealt with or how researchers seeking to address the challenges in this space would find information or start to make sense of it. So the language that we are choosing is critical for giving voice to data. It transforms that then into information and insight. So the ways that words are used certainly is a very powerful device. It can enable, but it can also constrain. So what we're seeking to do is really slow things down a little bit and enrich the process. I've always liked this statement from David Levy who's an information ethicist who worked for a long time as part of the Xerox PARC group of anthropologists and technologists seeking to understand human-computer interaction. And that very much has shaped the work I do as a social informaticist looking at the connection between people, information and emerging technologies. And his concern is that we have lost the time to look and to think at precisely the moment we started to develop these very advanced tools for investigation and communication. I certainly saw that as a bit of a backstory to me. So I trained as a librarian back in the 90s, became a teacher of librarians, particularly around classification and indexing, and then undertook a PhD in information science, information retrieval, where I was looking to understand the ways that researchers were using representations of ideas in these newly networked information systems. So it was an ethnographic exploration to try to get a better understanding of the sensemaking process that was related to finding what you are looking for. And the researchers that I was witnessing undertaking this work over to your process were breaking new ground, which is often the case when you're undertaking research. You're looking for information that you don't necessarily have a name for yet, but you're seeking to enrich your understanding and look for information to support your thinking. And what I found and what it confirms what a lot of people anecdotally would recognize is you often at first know what you don't want before you know what you want. Sometimes you can very quickly, when you're engaging with a representation of content online, you can make that decision instantaneous. But many times it comes through the slow unfolding and the interaction and engagement with those ideas as they are represented in an abstract or in a title. Sometimes you have to read the whole paper. So that takes time. It takes time to think about that. It takes time to link. The same thing is true in terms of trying to find a way to speak the same language. What I was witnessing was the person trying to find the way to speak the same language as the communities and ideas represented in those texts. And what we're talking about here in the work that Ruth and I have been doing is similar, trying to appreciate the context, trying to understand the communities who are represented or seeking to understand what is represented, meeting them on their own ground. And often because we're looking at issues around ethics and building trust and governance, you're talking about values and social practices that are very difficult to define in any distinct, discrete way, consistently across communities. So you're trying to map the context in the environment and really work towards co-constructing some sort of common understanding that also accommodates the fact that there will be multiple interpretations. So in this work, we're also looking, we've been inspired by not just the fair principles, but the ideas of care that come from the Global Indigenous Data Alliance, looking at different ways of baking in involvement with the community from the very beginning and continuously in the decisions that you're doing, ensuring that you have inclusive practices and giving the time to that, privileging that process, that aspect in the middle, that magic middle in some ways. It is translational work and that translational work takes a lot of time, it takes a lot of diplomacy. There's a need for mechanisms for feedback in all directions and also a need to listen and learn and adapt. With that, I'm going to turn over to Ruth. Okay, so I'm going to give a couple of examples of what we've been doing recently and how we've been trying to address that and why we also think that this is so important. So in the context of some of the more sort of technical discussions we've been hearing yesterday and today, it's really looking at the F in fair in a broader sense and the C in the care looking at the collective benefit and collective perspective on the data. So in terms of making it, making the information findable, it needs to be findable when you're talking about standards within the entire community that's consuming that standard and sometimes that can be extremely broad and when you're talking about setting out internal frameworks for the management and use of data and a lot of what Rob was saying in the previous presentation completely resonates with me in my day job doing data analytics in the learning space and having to reinvent the way that we tag our artifacts, our data artifacts every time so that we've got some understanding of how that should be interpreted in our analytics. So that's definitely part of it too. So when we're encouraging communities to standardize on terminologies we need to also look back about how does that link back into the wider community and be aware of that translational piece that is at some point going to be part of the work that you do if you're working together very closely as experts in your specific research fields there will be a point in which that it needs to be communicated and implemented back in the real world and we need to make sure that we're using language that is accessible to the affected people and also meets them on their own ground so that they have a real sense of participation in that otherwise we do get adoption and trust issues. So that's really where we are we're bridging that gap into the community and let me go back a minute just sorry and explain how so the first step of that is getting as I said understanding the context of where this information sits and who in the real world dare I say will be interacting with it or participating in the use of that language or that vocabulary at some point. So we sort of did quite a lot of analysis when we were putting standards together or a data use framework for a government organization in trying to understand where the recipients of that or the subjects of that data sit and that's a sort of very simplified version or sort of cut down version of the sort of diagram where you're looking at organizations the people linking those organizations the policies and documents and actual artifacts that are being generated by those and how people interact with those how that affects their day to day lives and then the language that's actually used within those and with that discovery process we're then able to start identifying who in the community needs to be part of the process of defining the vocabulary for that for our organization a government organization and how well the terminology and vocabulary that we're using in our framework will resonate with them and do we now need to have a separate set of vocabularies or terms that we can use in discussions with other groups in the broader community and so that's a co-creation piece and the first piece of that is actually understanding who's out there, what's the landscape and who should be involved in that discussion so that's the first sort of piece of work that needs to be done and then following on from that we can then start looking at the terms within our organization so then there was a fairly significant piece of work that Theresa and I did within the understanding of who the community is that's being affected and what the vocabulary sort of what resonates within those different communities and what doesn't we then start to look internally with that context and understand how vocabulary is being used within the organization or across the different parts of that organization so what you see here is and again for reasons of confidentiality we can't give you a sort of detailed view but what you see here is a sort of breakdown of documents which are the little green dots terms that are specified within those documents and then organizational departments or agencies that use those and what the interactions are it's actually turned out to be a much bigger and messier map than that so I just thought I'd have the simplified version for you to sort of see and then you can see by the colours of the lines whether they're producing or consuming those documents whether it's actually going to affect how they operate or whether they're defining the standards and terms by which other people operate and therefore they're the ones that own and define the vocabulary as well and then we can start to see how well shared those certain pieces of vocabulary are across the organization 15 minutes okay I will quickly go to the last slide I didn't realise coming out of time from a context of ISO a similar sort of operation took place but it was really about trying to not standardize on peoples on vocabulary but understand that and here's a very simple example with a simple term data set but depending on where you're coming from i.e. are you defining it in terms of genomics and geographic information or something that's addressing a much more broader church instead of broad information technology you're going to be finding the same word in slightly different ways we need to be able to keep to that because we're communicating that word out to our subject matter experts who are ultimately the data subjects and the broader community at the end of the day and we have to meet them on their ground so we have to find a way of linking these together and understanding their origins and their context and not over standardizing on one term and being too obsessed with getting that single term right because it never will be effectively and over time it will change as well so on that note what we're doing is trying to develop frameworks and skills development programs really to help people work through that co-creation piece and understand what's important and what's not in approaching a sort of broader more inclusive vocabulary discovery approach thank you