After logging into Rayyan, users are presented with a dashboard of all their current reviews (Fig. 2 ). They can either create a new review or work on an existing one. For each review, they upload one or more citation file obtained from searching different databases. Rayyan supports several standard formats, e.g., RefMan RIS and EndNote. At the outset, Rayyan processes the citation file by extracting different metadata, e.g., title, authors, and computing others, e.g., MeSH terms and language of the article, for each article or study in the citation file. These will then populate the facets in the review workbench (Fig. 3 ) to help explore and filter the studies. MeSH terms are presented as a word cloud allowing users to quickly grasp the main topics presented in the studies. In addition, users can filter studies based on two predefined lists of keywords that will most likely hint to either include or exclude a study. The user can also modify these two lists by removing and adding keywords, thus giving more flexibility in the labeling and selection of studies. Rayyan was seeded with two lists obtained from the EMBASE project to filter RCTs [14 ].
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4 ) in which the citations are represented as nodes in a graph and clustered based on how similar they are (using an edit distance) in terms of title and abstract content as well as common authors. The similarity thresholds can be tuned independently for each attribute, i.e., title, abstract, and authors, as well an overall threshold.
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Rayyan dashboard. The dashboard lists all reviews for this user as well as for each review the progress in terms of decisions made and estimated time spent working on the review for all collaborators
Rayyan workbench. The workbench shows the different ways users interact with the app
Similarity graph. Interacting with citations through the similarity graph
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