Revtool

[Natalí Stibb]

Researcherscommonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.

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The field of evidence synthesis is growing rapidly, with a corresponding increase in the number of software tools and workflows to support the construction of systematic reviews, systematic maps, and meta-analyses. Despite much progress, however, a number of problems remain, including slow integration of new statistical or methodological approaches into user-friendly software, low prevalence of open-source software, and poor integration among distinct software tools. These issues hinder the utility and transparency of new methods to the research community. Here, I present revtools, an R package to support article screening during evidence synthesis projects. It provides tools for the import and deduplication of bibliographic data, screening of articles by title or abstract, and visualization of article content using topic models. The software is entirely open-source and combines command-line scripting for experienced programmers with custom-built user interfaces for casual users, with further methods to support article screening to be added over time. revtools provides free access to novel methods in an open-source environment and represents a valuable step in expanding the capacity of R to support evidence synthesis projects.

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