 Hi, I'm Alex Bannack-Brown, based at the Quest Centre at the Berlin Institute of Health, and today I'm going to discuss research ecosystems and the role of R in effective evidence synthesis, how we can build bridges between researchers. To give you some background, I work in evidence synthesis in translational biomedicine. We take evidence from preclinical research and synthesize it to inform decisions for clinical research and to refine and improve our own experiments. Preclinical reviews and preclinical research are not very common, primary research reports are not reported in very standardized ways, and of course the evidence synthesis process is a very resource intensive one. Our aim is to create research ecosystems in biomedicine to increase good quality research and to improve the translation of biomedical findings to improve human health. Where preclinical research is informed by systematic reviews and where preclinical research is synthesizable. So what are research ecosystems? Open research ecosystems are communities of researchers, evidence synthesis, tool makers, information specialists, data managers, etc. that collaboratively recognize evidence synthesis as the end goal of research. Research ecosystems support researchers to design, undertake and report primary research and evidence synthesis in a way that optimizes reuse, translation and sharing of data and methods. And research ecosystems are based on shared, open principles, transparency of research methods and data, both in evidence synthesis and primary research. On the left here we've got the current evidence synthesis system. Reviewers are separated from the research process. Data could be missing if it is not published and then does not get synthesized. Synthesis is a resource intensive task and evidence synthesis also may be delayed in relation to when new data is available. On the right we've got our future goal that systematic reviewers are embedded within the research process, supporting researchers to conduct evidence synthesis in their own domain. Researcher is available for synthesis and may even be directly available for synthesis by passing reports in the traditional published manner. And the processes of evidence synthesis and the reports of evidence synthesis are also open to the public and stakeholders. To realize this idea we are working to build research ecosystems in translational biomedical research. A project excitingly we've recently got some funding to kick start. We're working to increase education about systematic reviews in biomedicine. We're working to provide widespread infrastructure for researchers, both to conduct systematic reviews and also to conduct reproducible primary research. We're bringing together stakeholders to support this, both preclinical researchers, systematic reviews, systematic review methodologists, statisticians, information specialists as well as patient advocates and others. We're working to create a community code of conduct for what it means to be part of such a research ecosystem and how we can best work together, including the best structure and format for this ecosystem and for the communication. And we're exploring additional infrastructure that may be needed in future in order to allow for the growth of the ecosystem. And infrastructure here is really key to supporting the widespread growth and uptake of evidence synthesis. And Brian Nosek's cultural change pyramid really highlights this. Infrastructure is the foundation allowing it to be possible and supporting all other steps so that we can build communities to make these practices normative. And R is a wonderful infrastructure framework and existing community that already supports and is closely linked to the principles of open evidence and open research ecosystems. And the number of tools that we already have available is highlighted in this amazing conference. We're building, testing, validating and integrating new tools all the time. We have tools to support the evidence synthesis process and tools to automate the steps in evidence synthesis process. We've got tools to engage with external databases and other tools. And we've also got tools to export our synthesis and share these results and findings with others. But surrounding this more broadly, we have groups and tools that support evidence synthesis frameworks and support these communities. We also have education and tools to support education, both for systematic review methodology, but also education on how to use these tools themselves. And most importantly, we have tools to support integrating primary research into the evidence synthesis pipeline. Existing tools for cleaning experimental data, building reproducible analysis code, and tools to help us disseminate our data and our research. And just wanted to draw attention to the talk that Emily Hennessy gave yesterday about making primary research synthesizable. And we can use our existing overlapping communities to build pipelines to make best use of the existing tools to collectively identify gaps for new tools and to share these best practices that we've got. On top of infrastructure and tools, it's important to build a strong community based around clear shared values. And that's something that we're working towards in translational biomedicine. Transparency of methods, of data, of research processes, ensuring quality that we foster open reproducible research, both primary as well as meta research. And that research and data are open and fair, findable, accessible, interoperable, and reusable. And that we have these best practices of workflows and best practices in data sharing in practical solutions. And that's something that relies on effective communication, using existing platforms for optimizing how we communicate. Slack, forums, conferences, et cetera. How we can set clear standards, values, and how we can shape our role as community members by setting codes of conduct to ensure that the community that we're a part of is fair for all. And support, how we can support each other in realizing these goals. A starting place that we're working with is fostering sharing and learning. For example, with introductory workshops to reproducible research in R, introductory workshops to systematic reviews and evidence synthesis. These are just some of the workshops we deliver at Charité in Berlin. And I think support is a great place to finish up. Because support both within the community, supporting each other. But also support from out with the community, out with the immediate community, engaging with other communities that can help us grow. And I think support really underpins a strong community and helps us achieve the goal of open research ecosystems. Not just in biomedicine, but for everyone. And just briefly, I've presented some of the work today that we're applying a research ecosystems and this approach in biomedicine. This is really work in progress. Thank you very much for listening and I'm really looking forward to hearing your thoughts and engaging in discussions on Slack. Thank you.