ACRT‐SCTS Scholar Abstracts
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC5350643 | biostudies-literature | 2010 Mar
REPOSITORIES: biostudies-literature
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Project description:Despite the rapid growth in the number of scientific publications, our understanding of author publication trajectories remains limited. Here we propose an embedding-based framework for tracking author trajectories in a geometric space that leverages the information encoded in the publication sequences, namely the list of the consecutive publication venues for each scholar. Using the publication histories of approximately 30,000 social media researchers, we obtain a knowledge space that broadly captures essential information about periodicals as well as complex (inter-)disciplinary structures of science. Based on this space, we study academic success through the prism of movement across scientific periodicals. We use a measure from human mobility, the radius of gyration, to characterize individual scholars' trajectories. Results show that author mobility across periodicals negatively correlates with citations, suggesting that successful scholars tend to publish in a relatively proximal range of periodicals. Overall, our framework discovers intricate structures in large-scale sequential data and provides new ways to explore mobility and trajectory patterns.
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Project description:Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citation database. For assessing the literature, databases, such as PubMed, PsycINFO, Scopus, and Web of Science, can be used in place of GS because they are more reliable. The aim of this study was to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified. For this purpose, 281 documents that cited 2 specific works were retrieved via Publish or Perish software (PoP) and were examined. This work studied the false-positive issue inherent in the analysis of neuroimaging data. The results revealed an unprecedented error rate, with 279 of 281 (99.3%) examined references containing at least one error. Nonacademic documents tended to contain more errors than academic publications (U=5117.0; P<.001). This viewpoint article, based on a case study examining GS data accuracy, shows that GS data not only fail to be accurate but also potentially expose researchers, who would use these data without verification, to substantial biases in their analyses and results. Further work must be conducted to assess the consequences of using GS data extracted by PoP.
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