 Hi, I'm Emily Hennessey and today I'm going to be talking about promoting synthesis ready research. So before I begin, I do want to mention that this was an idea and paper that was developed at the 2019 Evidence Synthesis Hackathon and we do have a pre-print available. I want to acknowledge all my co-authors on this truly collaborative endeavor. So before we dive into the topic, just a brief intro to the terms. First of all, synthesis ready is the term that we use to describe a study. It's metadata and data that can be readily and accurately used in an evidence synthesis. So those would be scoping reviews, systematic reviews, meta-analyses, or meta-reviews. Anything that is part of the evidence synthesis umbrella would be relevant here. And so really the idea for this paper is primarily generated from this underpinning of the open science principles and that is really having increased rigor, accountability, and reproducibility for research by having transparent and accessible knowledge study data that is shared across scientific networks. And so that's really important. You know, those of you that are here that are learning about doing evidence synthesis in R, you would not be able to do that if people did not share their data and share it accurately, share correct data. And so, you know, an additional principle that, set of principles that really helps research be synthesis ready, in addition to thinking about it as open science, following open science principles is this idea of research that is findable, accessible, interoperable, and reusable. And so from our perspective, open science is a great foundation, great groundwork. But if you're really approaching this from the view of making research fair, you will also be making your research synthesis ready, and therefore it will be much easier for evidence synthesis to use and it will be a better use of existing resources. So the goal overall is better scientific evidence, a very lofty endeavor. So in addition to, you know, improving science as a whole, there are some specific advantages for you in terms of not only operating from an open science perspective, but operating in a way that will make your research synthesis ready. So if it's synthesis ready, we consider it more likely to be identified by people that are doing a literature search for your study. And if it's, you know, lost somewhere and not archived well, then it may be very difficult to be identified and used in an evidence synthesis. And what that means kind of in the long term is that that could open up many potential opportunities for future collaborations of individuals that are interested in the same topics you're interested in, potential for research sharing because of those collaboration, as well as increasing kind of your lab or your team's recognition internationally. And finally kind of the hard point of academia is increased citations. If your study is identified and used in evidence synthesis, that's a citation that you did not have before that point. So from the perspective of making your research synthesis ready, we conceptualize that as a kind of a process that has to begin at study design and continue throughout the project life until you're ready to share it. You don't want to think about making your research synthesis ready when you're ready to publish the paper and share the data. You really want to build that into the initial piece of the study design in terms of making sure you have a study that's been pre-registered and that files a protocol. However, today's topic we're really going to be primarily focused on this idea of study dissemination and some tools you can use to make sure that your research is highly likely to be to be found and accessible and usable by evidence synthesis. So just as a brief introduction, it's really important at the initial study design and study conduct phase to use transparent and reproducible workflows. Because doing all of these steps well will make the later steps of producing synthesis ready research much, much easier. When it comes to study dissemination, we have kind of six primary tips for ways to help make your research more findable. And so initially having some sort of unique permanent identifier that's tied to both your data and your study publication such as the DOI for the data and the publication. And also for authors making sure to include the ORCID ID with the submission as institutional emails can often kind of be obsolete. It's really important when registering on different databases or journal websites to use consistent author user names so that folks who are interested in your work can easily find you in the work that you do. And finally, especially if you're sharing data separately from your published manuscript to include a data availability statement along with your manuscript so that anyone who finds your manuscript knows exactly where to find your data and how to get there is really important. And so, you know, another highlight of doing this is that your data can also be cited in addition to your published version of your manuscript. So the next steps are really when thinking about study dissemination, how to maximize the reach of your study that it will be identified in literature searches for your research. So if you have data stored in a repository, make sure to use tags that will tag kind of the topic of your research. And we actually suggest using an R package lit searcher that's developed by Eliza. There's a whole website and vignettes that go along with it, but essentially this is an evidence based way to use R to generate keywords that can be used in the title and the abstract and those journal keywords that can help researchers to identify your research much easier than words that you might think of on your own. Finally, in terms of really making sure that others can reuse your data in their evidence synthesis, it's really important and that you get credit for it. It's really important to think about licensing your data. We don't have the time to go over all those options now, but there are several websites, several organizations that will give you more information on that. And we actually chose for a recent study, the CCBY 4.0 license, which tends to be used most commonly in sharing primary study research. Along with that data license, you want to make sure that you've included wherever you share your data information, such as a data dictionary or variable crosswalks, and a read me file, which goes through exactly what readers and users of the data can expect to see when they have your data in hand. Lastly, in terms of thinking through where you want to archive and share your data, there are lots of different options. We have a list of them here in the paper. We actually go into the advantages and disadvantages of each, so I encourage you to view those. The best option would be a non-affiliated repository as it's likely the most findable provided it's free as well, so be sure to look in our resources for that. And so lastly, this is just the overall roadmap as a reminder, but please feel free to read the paper and the example that goes along with it because that will give a lot more examples around how to do this for your own research.