CiteSource enhances evidence synthesis reporting by optimizing search strategies in research, indicating decision-making improvements.
Evidence synthesis findings hinge upon well-designed, effective search strategies. When developing these strategies, evidence synthesis teams make multiple decisions (e.g., selecting information sources, developing search string architecture, and picking supplementary search methods) that directly affect the breadth of discovered evidence and thus evidence synthesis outcomes. Despite the number of decisions required when developing search strategies, limited guidance exists to inform these decisions using a data-driven approach. To help address this gap, we developed CiteSource, an R package and accompanying Shiny application, that supports data-driven search strategy development and reporting. CiteSource allows users to assign and retain metadata across three custom fields: source, label, and string to indicate where the records were found, what method or string was used to find them, and whether they were included after screening. CiteSource allows users to visually map the overlap between sets of records, create data summaries of citation records, and export citation records with the newly assigned metadata. CiteSource's analysis and visualization outputs can be harnessed for a variety of use cases, such as optimizing literature source selection, honing and understanding the effectiveness of search strings, and evaluating the impacts of literature sources and supplementary search methods. Overall, CiteSource provides a tool for evidence synthesizers to make informed data-driven decisions that boost the efficiency, rigor, and transparency of search strategies and associated reporting.
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Riley et al. (2026) studied this question.
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