 All right. I would now like to take the opportunity to welcome the second keynote speaker of the Sand Conference, Ann Lee Steele. Ann is the community manager for the Turingway project at the Allen Turing Institute, where she facilitates a collaborative resource for reproducible data science and supports an open source community in developing practices for researchers and practitioners around the world. She has worked on a variety of projects in the open ecosystem, including at the Internet Society, Wikimedia Deutschland and Open Knowledge Foundation, and is passionate about the capacity for open source practices to make research more accessible, collaborative and inclusive. Previously, she worked in the data journalism and education fields. Over to you, Ann. We are so fortunate to have you here. Thank you so much, Simon Duke. And thank you so much for the invitation to be here at the RSC ConAsia Australia, and with this opportunity to share reflections about open knowledge and my somewhat long and winding path towards it. When I was originally asked and invited to give this early keynote, I admit that my first reaction was, what can a community manager share with a community of research software engineers? And I sat with this question for a few weeks. And as I did, it became a much larger one. What was the connection between the worlds I'd inhabited before joining open science and open research that weren't necessarily computational and the worlds that I inhabit now, populated by data scientists and other data driven folk. And so I decided to get back to square one and map my own experiences over the past few years in the hope that someone might learn from my own or that something might be gleaned from it more broadly. And I realized that as I went and had gone from research to practice and back again, from the field of anthropology to journalism to civic technology projects to open knowledge, and then eventually back to open scholarship as I am now, I was combining the tools and practices and systems that gained from each one of these spaces. And so I suppose another title of this short talk could be mapping my own open journey from research to practice and back again. And yes, there will be quite a few maps in this short talk. And so just to start at the beginning, you'll see, and I promise I'll bring the turning way back in later. I'm an anthropologist by training. And that means that we study culture defined really broadly. We look at the foundational questions of what bind us to each other believing that there's, you know, a lot that's changed since times of old, but not really all that much. And we connect these individual experiences, these experiences of rituals of kinship of belonging to wider processes and to wider phenomena, looking at how the social norms within the communities that we inhabit can speak to broader claims about our society at large. And to learn about this means observing people for long periods of time, talking to them into something that's called ethnography, trying to figure out the differences between what people say and what people do. And as a student, many years ago, I had studied how local communities govern their natural resources in Bhutan and Nepal, especially in light of or sometimes even despite international climate regulation. And so when I left the academy and anthropology at first, it was actually to work with data. I was hungry to apply these perspectives in the world and joined a media lab of an international affairs think tank, which asked big questions of the world at large. We use data to learn more about it, and to support policymakers to visualize the issues and make these processes more accessible and understandable. And in the process, I learned about other places that data was being used, and about how it was being used, whether supporting human rights work, using technology all around the world, such as this group naming Shahidi here, or using satellite imagery to access and to assess environmental damage during wartime, as with the group Bellingcat. And citizen journalism, like these projects felt like an alternative to the status quo, more focused on the collective as a source of knowledge and the capacity for people to write history by and for and with each other. And in the process, I'd learned from different groups that were creating this work in real time, realizing that there was in many ways a community of communities that was producing and doing this work, and kept each other accountable to changing journalism, as we knew it, and holding power to account. And so really my first introduction to the open world. And what I'd learned from open source investigation and open news was one, hold the powerful to account using the tools that you know how to use. In that case, it was in journalism, using data, using data visualization. To collective power, in this case, always seem to come from collaboration, from collaborative practices that brought people all around the world together in a way that was only really possible because of the digital medium. And three, community is everywhere, even in the most unexpected of places. I hadn't expected to find it in data journalism or data visualization, but I did. And of course, I thought of this quote, when fact is by Margaret Mead, an anthropologist who said, Never believe that a few caring people can't change the world. For indeed, that's all whoever have. And so funnily enough, I eventually returned returned back to the Academy in the hope that I could take some of these learnings and these tools as a data journalist, and maybe refine the questions a little bit better. This seemed to be circling around answers I couldn't quite put my finger on. And I originally entered with the idea of doing human rights work, but soon myself studying the practices behind that culture was everywhere, including the halls of the United Nations. And there were particular rituals and ways of working that proceeded there. I wanted to learn about how the idea of human rights work aligned or sometimes even did it with the practice of human rights practice itself. But in the spring of 2020, the COVID-19 pandemic hit and we all found ourselves at home. And of course, found myself asking the same questions in the online space. And in October 2020, I participated in a humanitarian mapathon and edited a part of open street map, the Wikipedia of maps. And, you know, coming from a data journalism background, I'd realized that originally, I'd found would have found myself interacting with this project, asking questions like, you know, how can this map be used? And what is it for? In what context could it be applied? But now I was asking questions like, who makes this map? Who makes this tool? Why do they do it? What culture prevails in in this map of the world? I was thinking like an anthropologist again, rather than a data journalist, and was keen to try and combine her. Around the same time, I was lucky enough to be able to participate in what was called the Frictionless Data Fellowship through the Open Knowledge Foundation, where I was introduced to a lot of foundational questions about open science and open scholarship. And this is where I actually first began to try and think like an anthropologist, but also like a data journalist, and also kind of combined the theory and practice of both together, learned that interoperability in one field didn't necessarily look like interoperability within another. Reproducibility, what does that look like within the field of reproducibility? What does that look like within neuroscience, within physics, within the many other fields and fellows that were in this program? Of course, as introduced to the open access movement and to data best practices, and ultimately learned while I was learning about open street map and about the wide world of open knowledge and the people that created about the tools and practices and systems that define open science in the field that we're all a part of today. Had a little bit of a chance to be able to combine some of these skills together within the Wikimedia system. Again, really just filled with wonder with how in the world, open knowledge has come to be the movement that it has. And so really, the more that I was able to be involved with these projects, learning in really another set of learnings here. One is that open is always context dependent. And like it's important to never forget that. In many cases, open was sometimes called the by all and end all of a project. In other times, in need to be customized for specific use. But also learned that everything has a culture from open source communities like open street map or Wikipedia to the culture of science itself. And if there's anything that I'd learned from open scholars and from folks within the open source software world, it was that what you make is as important as how you make it. The theory always affects the practice. Of course, it always brought back this quote from my group me. So getting back to this map of mapping my own journey from research to practice and back again. Now enters the turnway. I'd soon realized when I joined this project and in many ways in retracing the my own experiences in these various projects and fields within open knowledge and open science within the open ecosystem were broadly, but the turning way itself was entering a culture in many ways that it aims to try and change culture to change science as we know it came for a cultural shift within the fields within scientific fields. On one hand, to make reproducibility as important as the number of papers published to change the culture of data science. Not so dissimilar from the data journalists I'd first interacted with in the open, not when they open data ecosystem. Of course, as you all know, reproducibility as a aim of culture change within open science stemmed from the crisis of reproducibility from the crisis of the culture of the crisis. And so similarly, with the sharing way kind of in these foundations, the project has been able to grow as it has since 2019. And it's with these foundations in reproducibility or the lack thereof, that Dr. Kersi Whitaker in 2019 gathered together some of her closest allies across neuroscience and other fields to create the initial book, the book on reproducible research. And it was with the second phase of Dr. Malvi Kesharen, who joined the project in the fall of 2019, that this book, which began with a book of reproducibility, was able to expand into six different books, realizing that to address the culture of science, which aims for reproducibility, or perhaps should, if it is aims to have cultural change, required many other aspects in order to do so. It required looking at the project design, the foundations of how and why you do your work. It has to address the communicating research of being able to reach the widest audience possible to be accessible for folks across different fields. It's about being creating a culture of collaboration that, you know, meets people where they are. And but at the same time, I was able to encourage the learning of new skills. Of course, it's also about asking the questions about ethics, and about the foundations of how and why for whom research is done. And finally, about documenting the best practices are used within the community in order to share them with others as well. And so in many ways, the questions being asked here is I learned more about the turnways community manager, where we're really the same questions that I've asked of day to journalism, of open knowledge of open source opens for software projects, is that how do you change culture? And what is collaboration with many people create? And in many ways, it creates books like the during way. And so there are many different ways that people have gotten involved. And in many ways, there are many, there became a culture of collaboration through a set of communities of communities, with mentors, with maintainers, with communicators with the book dash planning community with translation and localization team with folks who share and best practices. And all of these different ways of contributing reminded me very much of these foundational movements within open knowledge. And so on one hand, you know, the open as it was defined within my original interaction with the project, and with the world of open data was actually open for another means, open for improving equity, not only with an open scholarship, I realized, but this could apply really all across the ecosystem, that there are many different definitions, one of which is being used within the culture of science. And all of this, of course, should be used to further aims of equity, diversity, and inclusion. But another thing to point out here, though, is that this aim of creating and sustaining and maintaining open infrastructure was really with the aim of supporting this culture change, right? And so this includes, you know, bringing in domain esters, bringing in statisticians people, folks who work with data, but also very much aiming to communicate with and for the public. And coordinating all the folks involved in this process, of course, really, really difficult. And the more that I've learned from open scientists, the more that I realized the specific context of open science in which we operate and the barriers there. But really key to this is the humans behind that process. And that's where I think community managers and research software engineers can learn a lot from each other, as well as data stewards, research application managers, which was a term recently incubated, and a new job recently incubated at the Turing Institute. And it's all these different types of folks that form the infrastructure behind how research is done. And it's something that many other parts and many other aspects of the open ecosystem can learn from, what it actually means to support open, not necessarily just a practice of what you do with data, but the people behind that process in the first place. Another really integral aspect to this that I've learned from the Turing way is the importance of acknowledgement in this process, the act of crowdsourcing in many other contexts and open knowledge projects and open source investigative projects and may not necessarily address this question of acknowledgement in the same way. I've learned a lot from how open science, and in many ways, even the act of creating these slides themselves, built upon the work of others. And that acknowledgement process is really embedded all throughout the Turing ways of project, but also within so many other projects than the open science ecosystem. Just another example here of how this is done. And it's really through the same process that I think that the collaborative process is really what enables has enabled the project to kind of reach into the places that has so far. And so of course, what I've learned from the Turing way from open science, from all of you, and from speaking to folks, where research software engineers who are within open science who compose this research infrastructure, is that these folks are really interested in culture change that we all are, in many ways, part of changing the culture of research, and that maybe all of us together and collectively can change the world of research itself. And so in retracing my own journey, I was wondering what yours would look like as well, what eventually brought you into the space of research software engineering and into research infrastructure, and eventually into the culture change of science itself. And so mapping our experiences with open research, many ways I'd realized that mapping ourselves and our own experiences that got us here, maybe that exercise would be important for all of us in order to decide the best path forward collectively, and as a community. This is just my little plug. If you'd like to join the community at any point, we'd love to see you at one of our community events online, or in any space. But most importantly, thank you to the community of folks that have made a project like The Turnway. Thank you all for listening to me this morning or evening, afternoon, wherever you are. And thank you for being a part of the culture change science. Thank you so much. Thank you, Anne. I would now like to stop the recording so that we can move to the next part of the session.