Testing a Citation and Text-Based Framework for Retrieving Publications for Literature Reviews

We propose a citation- and text-based framework to conduct literature review searches. Given a small set of articles included in a literature review (i.e. seed articles), the first step of the framework retrieves articles that are connected to the seed articles in the citation network. The next step filters these retrieved articles using a hybrid citation and text-based criteria. In this paper, we evaluate a first implementation of this framework (code available at https://github.com/janinaj/lit-review-search) by comparing it to the conventional search methods for retrieving the included studies of 6 published systematic reviews. Using different combinations of 3 seed articles, on average we retrieved 71.2% of the total included studies in the published reviews and 82.33% of the studies available in the search database (Scopus). Our best combinations retrieved 87% of the total included studies, which comprised 100% of the studies available in Scopus. In 5 of the 6 reviews, we reduced the number of results by 34–88%, which in practice would save reviewers significant time, since the overall number of search results that need to be manually screened is substantially reduced. These results suggest that our framework is a promising approach to improving the literature review search process.Ope

Systematic Examination of Pre- and Post-Retraction Citations

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a stark decrease in post-retraction citations and that most of these citations came from countries different from the retracted article's country of publication.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post-retraction citations came from articles with disciplinary and geographical boundaries different from that of the retracted article.Ope

ReTracker: Actively and Automatically Matching Retraction Metadata in Zotero

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.Ope

MARC to Schema.org: Providing Better Access to UIUC Library Holdings Data

The Library has made ~5.5 million catalog records with associated holdings available for bulk download in multiple formats. This poster reports on the workflows developed, the serializations employed (and the mappings between formats), the approach used to integrate holdings, and the lessons learned.Ope

Exposing Library Holdings (meta)Data in RDF Using schema.org Semantics

The holdings and item data that are unique to individual libraries are not discoverable on the web.  This presentation discuss options to make essential library holdings and item data fully viable as linked open data using schema.org semantics.Ope