 Thank you. Hi, everyone. I'm very happy to be here. My name is Irene Ramos. I work as a data manager at the National Commission for the Knowledge and Use of Biodiversity in Mexico. And I'm going to share our experience developing an information system at Conabio. So Conabio is a governmental agency in Mexico. And we coordinate biodiversity data collection. And we manage this information through different information systems and citizen science platforms. Conabio is the bridge between government, academia, and society in topics related to biodiversity conservation and natural resources in general. For the past five years or so, we have been developing a new information system. This one is focused on agribiodiversity conservation. And it is part of a larger project, which has the global environment facility as the funding source and the food and agriculture organization as the implementing agency. This just means that FAO oversees the project from beginning to end. And they are also the institution who evaluates the project. What we do in Siagro is to generate and systematize open data related to native plants that are cultivated in traditional agriculture. For example, maize, beans, cacao, avocado, and among other delicious Mexican plants. And our ultimate objective is not just to have or to publish open and fair data. We also want to inform food security and conservation policies. We work with very diverse data. We have everything from quantitative biological variables to more qualitative information, for example, recipes of food. And the sources are also very different. We work with researchers who go in the field and collect data, very often in collaboration with local communities. But we also have information that external institutions have donated for our system. So it has been a very big challenge to demonstrate the value of the system to a community that is very diverse. Throughout the development of Siagro, it has been under scrutiny by funders. And we regularly have to report our progress. And this is normal for any project that has external funding. But in addition, we are also accountable to a wider community that includes students, researchers, even other governmental organizations, civil society organizations, research institutions, and other areas within Conabio. They participate in the system with different roles, different interests, and expectations. For example, external institutions might care more about the increased visibility of their data when it is incorporated into the system. But researchers might care more about their workflows and whether they become more efficient when they adopt our tools. So how do we demonstrate the value to this diversity of interests and expectations? We have taken inspiration from this paper, which is about how Elixir, this is a big European project. And in this paper, they narrate their journey of demonstrating the value to different stakeholders. However, our project is much smaller. It's not as well-established. And we work in a context where, well, in a region where there aren't as many incentives and there are many big obstacles to develop open infrastructure. So I'm going to tell you three lessons that we have learned and that have worked for us. The first is to define the narrative. Whenever we introduce Yagro, we talk about three dimensions of the system. The technical side includes data and tools. These are perhaps the most obvious. We do clarify that with respect to software. We are not only including the data portal or interactive visualizations. We also include the development and maintenance of the backend software. Everything that makes the system works and that is much less visible than shiny visualizations. And we also make it very explicit that there is a human dimension that is just as important. This is perhaps the component of the system that is most often overlooked. And I know maybe not here in this audience, but outside the bubble of the open science community, it's very difficult to make the case that this is just as important as the technical side. And so defining a narrative for us has come down to making visible the components of the system that are not a Cicillicine. And this has made a difference, especially in the early stages of the project, where our efforts were more invested in data collection and in development. And so we didn't have any tangible thing to show. And it was difficult for our collaborators to appreciate the progress that we were making, especially the progress in capacity building. This is the image that we want our collaborators to take away with them whenever we talk about the system. We want them to know that it's not just a collection of tools and data that are disconnected and that work alone. We want them to know that there's a workflow from collection to publication and that there are people at every step of the way. Sometimes we have needed to go one step further. We were kind of trapped in a circular problem where, in order to promote the value of the system, we first needed people to know about the system. But we needed to make the case that those outreach activities were necessary so that people knew about the system. I think this in other open projects, for example, when trying to make the case that people need training in open data or any other open topics, first, people need to at least understand what open is. But that is not possible if they haven't received any training. So that's the circular problem where we were trapped. And we decided that first, we needed to build very basic vocabulary, common vocabulary, to be able to have conversations about data and about infrastructure. So we organized a series of workshops on data literacy skills. And these were tailored to students and researchers who collaborate as data collectors. But we also invited people in the organization who are higher up and who are responsible for making decisions about project direction and about budgets. And this allowed us to build that common ground and to have conversations about what infrastructures do and what value they bring. And this series of workshops were developed with the mentorship of the Open Life Science Program. The third strategy was to establish feedback processes. With users, we did this by organizing service to understand their needs. And we also organized focus groups so that members of the community had a space to share experiences between them. This was important because in these spaces, it was no longer us telling them that the system was valuable or that the tools were useful. They were hearing that from people who had a similar background to them. So they were hearing about best practices and about tips for using the system and about the context where different tools worked better than other tools. So that was an experience that they told us was very enriching for them. And with funders and evaluators, this was a bit more informal process. Here, we noticed attention between the indicators that we were formally required to report that were related to the number of databases or to the number of publications versus what evaluators really cared about, which was the use and adoption of the system, which is much harder to measure. And simply asking the question of what they were understanding as an information system, regardless of what was on the proposal or what was on the indicators or on paper, gave us a lot of insight into what they were expecting to hear from us. And we started to report on outreach activities and training activities, even though they never were part of any formal indicators. And so this takes me back to the first point, because it is through these feedback processes that we can then refine and adapt the narrative of what we want to communicate about Siagro and about what it can do. And we can appeal to the specific interests of different members of the community, as well as design better training activities. So we have learned a lot along the way about implementing these strategies more deliberately instead of improvising along the way. And I hope that some of these ideas are useful, especially for projects that are working in a similar context or that are also relatively small. However, I don't know how much of this has translated into an appreciation of infrastructures more generally. I don't know if our users and our funders just take away that Siagro is useful, but maybe they don't generalize into we're not talking just about Siagro, we're talking about infrastructures for data in general. And so I keep thinking about what we can do at a broader scale so that smaller projects like ours don't have to do that double work of advocating for the value of infrastructure, as well as the actual work of developing and maintaining the infrastructure. And we know of some initiatives, like the one that was mentioned in the talk before, the recommendations for open science by UNESCO that has a section specifically for infrastructure, which mentions the need for funding and other things. But we still see a big gap between those recommendations and our day-to-day work. So I would be really curious to hear if you have some ideas. And to finish, I just want to thank my collaborators in Siagro. We are a small team within the organization, but we collaborate with many other areas. Thank you. Thank you. We now have time for questions. Any questions? Go ahead. What's your strategy for the next month, year, two years, like in the short term, enlarge them to the project doesn't die? That is out of my hands, unfortunately, because this is a project that, as many projects with external funding, has a funding period, a defined funding period. What I could like to do is to focus our efforts into that strategy of advocacy through training, because now that the project is in a more mature state, that we actually now have data, we have tools, and I wish we could try to use training to design different community engagement paths for different types of users, like users who are more technical, users who might just want to use visualizations to interact with the data. So think about different strategies that we can use to engage a wider community. Thank you. Any other questions? OK, thank you so much. You now have some time between your next session. Thank you.