 Hi, everyone, and thank you for joining us for a brief presentation on a project that me and Tim here have been working on for the past six or seven months, analyzing Canadian ecological restoration literature with bibliometric analysis and a systematic map. In this presentation, we will first discuss our overall approach, followed by our use of biblioshiny, which is a tool in our environment that is used for conducting bibliometric analyses, and then how we took that data and find it into a systematic mapping approach using programs such as Kadema, Tidyverse, and QGIS, and used it to extract meaningful information about the trends in Canadian ecological restoration literature and identify opportunities for the future. So this project was funded by the Tri-Council agencies, which is a national funding body in Canada, and they have a specific pool of funds for knowledge synthesis, so we were given some to synthesize Canadian ecological restoration research. We were given a pretty broad mandate, Dorian and I, when we were assigned this project. The first task we had was to kind of define what is ecological restoration, what do we mean by that? We ended up going with a very inclusive, big tent approach. We include remediation, reclamation, rehabilitation, rewilding, reforestation, in with our idea of restoration. So this search string you see below is one that we use to harvest a bunch of results from scopas. It's a very broad search string. The second question we had to answer is, what is Canadian? We ultimately landed on studies are Canadian if they take place in Canada, or if they have at least one researcher with a Canadian affiliation. Then the third question we had to answer is, what is research? What do we mean by that? We were interested in both peer reviewed and gray literature. Restoration is a practice, and a lot of it is documented in the gray literature. So we included searches in the systematic map portion that captured some of that. Our overall approach was one called research weaving, which was documented by Nakagawa A.L. at 2019. This answers the questions, both what is studied and what is published. The first phase looked at that what is published piece of it through a bibliometric analysis, which is mentioned as a very wide search that looks at everything that was published on ecological restoration that is Canadian. Phase two was a more narrow look. It was a systematic map that looked at the outcomes of ecological interventions documented in these studies, and we combined both of those to come up with our report. So our Bibliometric analysis was conducted in Biblioshining, which is an R program. The Biblioshining interface was easy to use, was clean and intuitive. It allowed us to import our large data sets of literature from Scopus for us because Scopus was compatible with Biblioshining. And it included metadata about author year, institution, keywords, and other metadata such as citations. Biblioshining offers many options for data analysis that explore the structures of knowledge, and they group those into three main categories. So the first is conceptual, which refers to the relationships between themes and keywords. So one example is shown here. Program allows us to create keyword for occurrence network analysis, where in the centrality and size of the terms and their bubbles represents their frequency and prevalence within the literature data set. The width of the connections represents the strength of those connections, and the colors are also program generated to represent different thematic bodies. And options for all of things such as layout, normalization, and clustering are all available for all types of figures that you can create. Another example is the thematic evolution map, which gives you options to divide into time slices and visualize the evolution of themes over time. The intellectual knowledge structure refers to the relationships between papers and intellectual output. So here we see a co-citation network analysis, which is similar to the keyword for occurrence network analysis, but it draws connections between citations and references to differentiate and group themes. Another example is the historical direct citation graph, which takes the themes and plus them as a function of time to get an idea of the temporal evolution of citation derived themes. The third null structure is the social one, and it refers to the relationships between authors, institutions, and countries. The show in here is the collaboration between authors and countries in a network web. And we also have one that is on a projected world map that shows the connections between countries and how many times authors cite each other. Some of the challenges with using BiblioShiny included screening through the Scopus Drive data set, which was not an option in BiblioShiny, requiring an extra step, in our case, exporting to and screening in Excel, which was not as user-friendly as it could have been, and then re-uploading it to BiblioShiny. The figure quality was also quite low in BiblioShiny, which became a problem when keywords or citations within a crowded network web were small, fuzzy, or difficult to reach. And finally, the options for graphical personalization were, unfortunately, pretty limited, making it difficult to customize outputs in a useful way. And for the systematic map, we narrowed our search to focus on papers that documented the outcomes of ecological interventions. We used Kadima, which is a web-based platform that helps you manage systematic maps and reviews. Kadima allows you to input a RIS file from each search that you do. And it keeps track of the search strings. It keeps track of the exclusions as you do screening. This is very helpful for producing the final report and being able to tell people when certain exclusions were made. Also has this handy data extraction screen, which allowed us to extract data in certain categories that we cared about. One of the big challenges of this part was being able to provide multiple categorical data inputs. So for example, the research province here, we often had studies that spanned multiple provinces, but the dropdown menu doesn't let you select multiple provinces. So it does let you input plain text as in the other field. So we decided to use semicolons for data separation, but that meant we had to use tidyverse to clean up our data in R before we could make the heat maps and bar charts. This extra step limited us from using EV Atlas, which is a program that automates these steps. We also used QGIS to map some of these results on a physical map. It was relatively simple to convert all the coordinates. Every study kind of uses its own system, but you can go through and convert those to one system. So these are some examples of the graphics we were able to produce heat maps, which show where a couple categories line up in the studies that we looked at. We also were able to produce some bar graphs that show the count of articles by different things, particularly noticeable here that a lot of the restoration articles were concentrated in Alberta, Ontario and Quebec, which is an interesting result because there is a lot of degradation and restoration work in other places, especially the Yukon, Nunavut and Northwest Territories. There's a lot of mining activity, but not a lot of restoration studies. So bringing it all together, Ghibli Ashani really provided the groundwork and it highlighted trends, relationships, connections between individuals, ideas and locations. Systematic mapping allowed us to extract finer scale information about ecosystems, methodologies and outcomes. Next step is to take that information, make it accessible with something like a searchable WordPress plugin that allows practitioners and other interested parties to filter the literature by desired ecosystem type, time period or target species, for example. Our team as a whole will be developing a comprehensive report using not only our literature derived results, but also results and themes derived from interviews and practitioners and from case studies from across Canada. So thank you all for listening and taking part today. And we look forward to any questions from you. Thanks so much.