Author : Qing Ke
Traditionally, the number of citations that a scholarly paper receives from other papers is used as the proxy of its scientific impact. Yet citations can come from domains outside the scientific community, and one such example is through patented technologies—paper can be cited by patents, achieving technological impact.
While the scientific impact of papers has been extensively studied, the technological aspect remains largely unknown. Here we aim to fill this gap by presenting a comparative study on how 919 thousand biomedical papers are cited by U.S. patents and by other papers over time.
We observe a positive correlation between citations from patents and from papers, but there is little overlap between the two domains in either the most cited papers, or papers with the most delayed recognition.
We also find that the two types of citations exhibit distinct temporal variations, with patent citations lagging behind paper citations for a median of 6 years for the majority of papers. Our work contributes to the understanding of the technological, and societal in general, impact of papers.
URL : https://arxiv.org/abs/1804.04105
Authors : Qing Ke, Yong-Yeol Ahn, Cassidy R. Sugimoto
Metrics derived from Twitter and other social media—often referred to as altmetrics—are increasingly used to estimate the broader social impacts of scholarship. Such efforts, however, may produce highly misleading results, as the entities that participate in conversations about science on these platforms are largely unknown.
For instance, if altmetric activities are generated mainly by scientists, does it really capture broader social impacts of science? Here we present a systematic approach to identifying and analyzing scientists on Twitter.
Our method can identify scientists across many disciplines, without relying on external bibliographic data, and be easily adapted to identify other stakeholder groups in science.
We investigate the demographics, sharing behaviors, and interconnectivity of the identified scientists.
We find that Twitter has been employed by scholars across the disciplinary spectrum, with an over-representation of social and computer and information scientists; under-representation of mathematical, physical, and life scientists; and a better representation of women compared to scholarly publishing.
Analysis of the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a small fraction of shared URLs are science-related. We find an assortative mixing with respect to disciplines in the networks between scientists, suggesting the maintenance of disciplinary walls in social media.
Our work contributes to the literature both methodologically and conceptually—we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists, thus providing foundational information for the construction and use of indicators on the basis of social media metrics.
URL : A systematic identification and analysis of scientists on Twitter
DOI : https://doi.org/10.1371/journal.pone.0175368