A tipping point for open citation data

uCite: The union of nine large-scale public PubMed citation datasets with reliability filtering

There has been a recent push to make public, aggregate, and increase coverage of bibliographic citation data. Here we describe uCite, a citation dataset containing 564 million PubMed citation pairs aggregated from the following nine sources: PubMed Central, iCite, OpenCitations, Dimensions, Microsoft Academic Graph, Aminer, Semantic Scholar, Lens, and OpCitance. Of these, 51 million (9%) were labeled unreliable, as determined by patterns of source discrepancies explained by ambiguous metadata, crosswalk, and typographical errors, citing future publications, and multi-paper documents. Each source contributes to improved coverage and reliability, but varies dramatically in precision and recall, estimates of which are contrasted with the Web of Science and Scopus herein.

OpCitance: Citation contexts identified from the PubMed Central open access articles

OpCitance contains all the sentences from 2 million PubMed Central open-access (PMCOA) articles, with 137 million inline citations annotated (i.e., the "citation contexts"). Parsing out the references and citation contexts from the PMCOA XML files was non-trivial due to the diversity of referencing style. Only 0.5% citation contexts remain unidentified due to technical or human issues, e.g., references unmentioned by the authors in the text or improper XML nesting, which is more common among older articles (pre-2000). PubMed IDs (PMIDs) linked to inline citations in the XML files compared to citations harvested using the NCBI E-Utilities differed for 70.96% of the articles. Using an in-house citation matcher, called Patci, 6.84% of the referenced PMIDs were supplemented and corrected. OpCitance includes fewer total number of articles than the Semantic Scholar Open Research Corpus, but OpCitance has 160 thousand unique articles, a higher inline citation identification rate, and a more accurate reference mapping to PMIDs. We hope that OpCitance will facilitate citation context studies in particular and benefit text-mining research more broadly.

Robustness of evidence reported in preprints during peer review

Scientists have expressed concern that the risk of flawed decision making is increased through the use of preprint data that might change after undergoing peer review. This Health Policy paper assesses how COVID-19 evidence presented in preprints changes after review. We quantified attrition dynamics of more than 1000 epidemiological estimates first reported in 100 preprints matched to their subsequent peer-reviewed journal publication. Point estimate values changed an average of 6% during review; the correlation between estimate values before and after review was high (0·99) and there was no systematic trend. Expert peer-review scores of preprint quality were not related to eventual publication in a peer-reviewed journal. Uncertainty was reduced during peer review, with CIs reducing by 7% on average. These results support the use of preprints, a component of biomedical research literature, in decision making. These results can also help inform the use of preprints during the ongoing COVID-19 pandemic and future disease outbreaks.