Scientific retractions occur for a multitude of reasons. A growing body of research has studied the phenomenon of retraction through systematic analyses of the characteristics of retracted articles and their associated citations. In our study, we focus on the characteristics of articles that cite retracted articles, and the changes in citation dynamics pre‐ and post‐retraction. We leverage descriptive statistics and ego‐network methods to examine 4,871 retracted articles and their citations before and after retraction. Our retracted articles data was obtained from PubMed, Scopus, and Retraction Watch and their citing articles from Scopus. Our findings indicate a stark decrease in post‐retraction citations and that most of these citations came from countries different from the retracted article's country of publication. Citation context analyses of a subset of retracted articles also reveal that post‐retraction citations came from articles with disciplinary and geographical boundaries different from that of the retracted article.

Citations between papers and patents reflect transfer of knowledge between science and technology. Patents commonly cite papers but papers rarely cite patents. Here, we identified 6,033 paper‐to‐patent citations in a collection of 1.5 million PubMed Central open access articles. These citing papers and cited patents contained 132,536 paper‐to‐paper, 200,339 patent‐to‐patent, and 36,342 patent‐to‐paper citations. These four citation datasets were used to model the temporal patterns of knowledge transfer within and across patents and papers. We found that the cited patents are generally much older than the cited papers, regardless of whether they are cited by papers or patents. Discipline, affiliation type, and self‐citation also affect the age of the cited papers and patents. The recency of the citations partly explains the asymmetry in citations between papers and patents.

Retraction removes seriously flawed papers from the scientific literature. However, even papers retracted for scientific fraud continue to be cited and used as valid after their retraction. Retracted papers are inadequately identified on publisher pages and in scholarly databases, and scholars' personal libraries frequently contain retracted papers. To address this, we are developing a tool called ReTracker (https://github.com/nikolausn/ReTrackers) that automatically checks a user's Zotero library for retracted articles, and adds retraction status as a new metadata field directly in the library. In this paper, we present the current version of ReTracker, which automatically flags retracted articles from PubMed. We describe how we have iteratively improved ReTracker's matching performance through its initial two versions. Our tests show that the current version of ReTracker is able to flag retracted articles from PubMed with high precision and recall, and to distinguish retracted articles from articles about retraction. In its current state, ReTracker can actively and automatically bring retraction metadata into Zotero, and in future work we will test its usability with scholars.

Systematic reviews answer specific questions based on primary literature. However, systematic reviews on the same topic frequently disagree, yet there are no approaches for understanding why at a glance. Our goal is to provide a visual summary that could be useful to researchers, policy makers, and health care professionals in understanding why health controversies persist in the expert literature over time. We present a case study of a single controversy in public health, around the question: “Is reducing dietary salt beneficial at a population level?” We define and visualize three new constructs: the overall evidence base, which consists of the evidence summarized by systematic reviews (the inclusion network) and the unused evidence (isolated nodes). Our network visualization shows at a glance what evidence has been synthesized by each systematic review. Visualizing the temporal evolution of the network captures two key moments when new scientific opinions emerged, both associated with a turn to new sets of evidence that had little to no overlap with previously reviewed evidence. Limited overlap between the evidence reviewed was also found for systematic reviews published in the same year. Future work will focus on understanding the reasons for limited overlap and automating this methodology for medical literature databases.