metadat is an R package that collates and acts as a repository for meta-analytic data across diverse research fields (e.g., psychology, education, ecology, evolution). These data can be used freely for meta-analytic training to demonstrate concepts and complex analyses, re-analysis, and updating past meta-analyses. Meta-analysts from any field can contribute data and/or examples of data use.
Evidence synthesis (ES) is the process of identifying, collating and synthesising primary scientific research (such as articles and reports) for the purposes of providing reliable, transparent summaries. The goal of this project is to collect, integrate and expand the universe of available functions for ES projects in R, via our proposed metaverse package. Like tidyverse, metaverse is envisioned as a collector package that makes it straightforward to install a set of functions – currently located in separate packages – for a common purpose.
Paperweight, driven by a combination of natural language processing (NLP) algorithms. In the evidence synthesis process, the first steps typically require reviewers to manually build a database of articles and journals they want to summarize. This process entails an exhaustive search of Google Scholar using manually chosen keywords. This approach is vulnerable to bias since the reviewer might be more likely to find certain articles or journals in their review over other ones, depending on the selected search keywords. Tackling this problem, Paperweight seeks to remove the need for a reviewer to manually choose keywords to form their search queries.
Full-text PDFs are almost always the most reliable source of information from academic articles. Even though several resources allow for the extraction of data from full-text documents, most of the time the information is incomplete, inaccurate, or not available. PDFs were created to look great, not to extract data from. So, when you try to copy/paste from PDF you often get unexpected results. In this first version the project allows users to easily copy text from a PDF and attempts to automatically identify the references.
robvis is an R package that allows users to quickly visualise risk-of-bias assessments performed as part of a systematic review. It allows users to created weighted bar-plots of the distribution of risk-of-bias judgements within each bias domain, in addition to “traffic light” plots of the specific domain-level judgements for each study. The resulting figures are formatted according the risk-of-bias assessment tool use to perform the assessments (currently supported tools are ROB-2, ROBINS-I and QUADAS-2). An associated Shiny app provides a user-friendly interface for the tool.
Defining a good search strategy for systematic reviews can be a particularly challenging task. Some of the problems encountered are: when asking two people for a strategy they will get totally different outputs, the number of hits is prohibitively high, there are missing relevant references because a specific keyword was omitted, few means of validating search strategies exist, it is difficult to adapt the strategy for other databases, errors may be introduced when adapting strategies between databases, etc.